Horizontal Review on Video Surveillance for Smart Cities: Edge Devices, Applications, Datasets, and Future Trends
Open accessAbstract
The automation strategy of today’s smart cities relies on large IoT (internet of Things) systems that collect big data analytics to gain insights. Although there have been recent reviews in this field, there is a remarkable gap that addresses four sides of the problem. Namely, the application of video surveillance in smart cities, algorithms, datasets, and embedded systems. In this paper, we discuss the latest datasets used, the algorithms used, and the recent advances in embedded systems to form edge vision computing are introduced. Moreover, future trends and challenges are addressed.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Some survey papers highlight the benefits of using specific datasets in VSSs.
“Some survey papers highlight the benefits of using specific datasets in VSSs [8,9].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [8]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
1 retracted
RETRACTED CHAPTER: Secure IoT Systems Using Raspberry Pi Machine Learning Artificial Intelligence
Babu, R. G., Karthika, P., & Rajan, V. A. (2020) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-37051-0_89 · Cited by 22 · see citing works
RetractedLeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · see citing works
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
Learning Spatiotemporal Features with 3D Convolutional Networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.510 · Cited by 9,593 · see citing works
Edge computing: Vision and challenges
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733 · see citing works
Two-Stream Convolutional Networks for Action Recognition in Videos
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756 · see citing works
Fog computing and its role in the internet of things
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012) · Proceedings of the first edition of the MCC workshop on Mobile cloud computing · doi:10.1145/2342509.2342513 · Cited by 6,244 · see citing works
3D Convolutional Neural Networks for Human Action Recognition
Ji, S., Xu, W., Yang, M., & Yu, K. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,222 · see citing works
Visual categorization with bags of keypoints
Csurka, G. (2004) · Cited by 4,379 · see citing works
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., & Gool, L. V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46484-8_2 · Cited by 4,164 · see citing works
Learning realistic human actions from movies
Ivan Laptev, A., Marszałek, M., Schmid, C., & Rozenfeld, B. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587756 · Cited by 3,481 · see citing works
Action Recognition with Improved Trajectories
Wang, H., & Schmid, C. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.441 · Cited by 3,407 · see citing works
Convolutional Two-Stream Network Fusion for Video Action Recognition
Feichtenhofer, C., Pinz, A. J., & Zisserman, A. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.213 · Cited by 2,842 · see citing works
The recognition of human movement using temporal templates
Bobick, A., & Davis, J. W. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.910878 · Cited by 2,830 · see citing works
Ivan Laptev, A. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-1838-7 · Cited by 2,631 · see citing works
ActivityNet: A large-scale video benchmark for human activity understanding
Heilbron, F. C., Escorcia, V., Ghanem, B., & Niebles, J. C. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298698 · Cited by 2,578 · see citing works
Action recognition by dense trajectories
Wang, H., Klaser, A., Schmid, C., & Liu, C.-L. (2011) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2011.5995407 · Cited by 2,459 · see citing works
Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
Zhang, C., Li, P., Sun, G., Guan, Y., Xiao, B., & Cong, J. (2015) · Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays · doi:10.1145/2684746.2689060 · Cited by 2,158 · see citing works
Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
Qiu, Z., Yao, T., & Mei, T. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.590 · Cited by 1,944 · see citing works
A Spatio-Temporal Descriptor Based on 3D-Gradients
Klaeser, A., Marszałek, M., & Schmid, C. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.99 · Cited by 1,842 · see citing works
Fisher Kernels on Visual Vocabularies for Image Categorization
Perronnin, F., & Dance, C. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383266 · Cited by 1,641 · see citing works
A Survey on the Edge Computing for the Internet of Things
Yu, W., Liang, F., He, X., Hatcher, W. G., Lu, C., Lin, J., & Yang, X. (2018) · IEEE Access · doi:10.1109/access.2017.2778504 · Cited by 1,520 · cited 2× · see citing works
“[16] conducted a detailed review, exploring how edge computing enhances IoT network efficiency.”
BackgroundRethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification
Xie, S., Sun, C., Huang, J., Tu, Z., & Murphy, K. P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01267-0_19 · Cited by 1,494 · see citing works
The “Something Something” Video Database for Learning and Evaluating Visual Common Sense
Goyal, R., Kahou, S. E., Michalski, V., Materzynska, J., Westphal, S., Kim, H., Haenel, V., Fruend, I., Yianilos, P., Mueller-Freitag, M., et al. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.622 · Cited by 1,471 · see citing works
A general framework for object detection
Papageorgiou, C., Oren, M., & Poggio, T. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1998.710772 · Cited by 1,437 · see citing works
Representing shape with a spatial pyramid kernel
Bosch, A., Zisserman, A., & Muñoz, X. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282340 · Cited by 1,407 · see citing works
Mobile edge computing, Fog et al.: A survey and analysis of security threats and challenges
Roman, R., Lopez, J., & Mambo, M. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2016.11.009 · Cited by 1,338 · see citing works
Privacy preserving crowd monitoring: Counting people without people models or tracking
Chan, A. B., Liang, J., & Vasconcelos, N. M. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587569 · Cited by 1,198 · see citing works
Secure integration of IoT and Cloud Computing
Stergiou, C. L., Psannis, K. E., Kim, B., & Gupta, B. B. (2016) · Future Generation Computer Systems · doi:10.1016/j.future.2016.11.031 · Cited by 1,114 · see citing works
Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding
Sigurdsson, G. A., Varol, G., Wang, X., Farhadi, A., Laptev, I., & Gupta, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46448-0_31 · Cited by 1,098 · see citing works
Learning To Count Objects in Images
Lempitsky, V., & Zisserman, A. (2010) · Cited by 1,063 · see citing works
Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs
Shou, Z., Wang, D., & Chang, S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.119 · Cited by 981 · see citing works
Long-Term Temporal Convolutions for Action Recognition
Varol, G., Laptev, I., & Schmid, C. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2712608 · Cited by 979 · see citing works
Temporal Action Detection with Structured Segment Networks
Zhao, Y., Xiong, Y., Wang, L., Wu, Z., Tang, X., & Lin, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.317 · Cited by 892 · see citing works
Survey on fog computing: architecture, key technologies, applications and open issues
Hu, P., Dhelim, S., Ning, H., & Qiu, T. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.09.002 · Cited by 880 · see citing works
Age Synthesis and Estimation via Faces: A Survey
Fu, Y., Guo, G., & Huang, T. S. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.36 · Cited by 749 · see citing works
Robust real-time periodic motion detection, analysis, and applications
Cutler, R., & Davis, L. S. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868681 · Cited by 701 · see citing works
Feature Mining for Localised Crowd Counting
Chen, K., Loy, C. C., Gong, S., & Xiang, T. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.21 · Cited by 691 · see citing works
Smart Cities: A Survey on Data Management, Security, and Enabling Technologies
Gharaibeh, A., Salahuddin, M. A., Hussini, S. J., Khreishah, A., Khalil, I., Guizani, M., & Al-Fuqaha, A. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2736886 · Cited by 628 · see citing works
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
Laroca, R., Severo, E. B., Junior, L. A. Z., Oliveira, L. S., Gonçalves, G. R., Schwartz, W. R., & Menotti, D. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489629 · Cited by 622 · see citing works
Smart cities ranking: an effective instrument for the positioning of the cities?
Giffinger, R., & Gudrun, H. (2010) · ACE Arquitectura Ciudad y Entorno · doi:10.5821/ace.v4i12.2483 · Cited by 611 · see citing works
Edge computing technologies for Internet of Things: a primer
Ai, Y., Peng, M., & Zhang, K. (2017) · Digital Communications and Networks · doi:10.1016/j.dcan.2017.07.001 · Cited by 592 · see citing works
Human age estimation using bio-inspired features
Guo, G., Mu, G., Fu, Y., & Huang, T. S. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206681 · Cited by 588 · see citing works
End-to-End Learning of Action Detection from Frame Glimpses in Videos
Yeung, S., Russakovsky, O., Mori, G., & Fei-Fei, L. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.293 · Cited by 585 · see citing works
Human Action Recognition Using Factorized Spatio-Temporal Convolutional Networks
Sun, L., Jia, K., Yeung, D., & Shi, B. E. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.522 · Cited by 580 · see citing works
Zhan, B., Monekosso, D., Remagnino, P., Velastín, S. A., & Xu, L.-Q. (2008) · Machine Vision and Applications · doi:10.1007/s00138-008-0132-4 · Cited by 552 · see citing works
Moments in Time Dataset: One Million Videos for Event Understanding
Monfort, M., Vondrick, C., Oliva, A., Andonian, A., Zhou, B., Ramakrishnan, K., Bargal, S. A., Yan, T., Brown, L., Fan, Q., et al. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2901464 · Cited by 537 · see citing works
Deep Label Distribution Learning With Label Ambiguity
Gao, B.-B., Xing, C., Xie, C.-W., Wu, J., & Geng, X. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2689998 · Cited by 511 · see citing works
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence
Nguyen, T.-N., & Meunier, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00136 · Cited by 481 · see citing works
Smart cities with big data: Reference models, challenges, and considerations
Lim, C., Kim, K.-J., & Maglio, P. P. (2018) · Cities · doi:10.1016/j.cities.2018.04.011 · Cited by 479 · see citing works
Cumulative Attribute Space for Age and Crowd Density Estimation
Chen, K., Gong, S., Xiang, T., & Loy, C. C. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.319 · Cited by 460 · see citing works
Ivan Laptev, A., & Pérez, P. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409105 · Cited by 417 · see citing works
DAPs: Deep Action Proposals for Action Understanding
Escorcia, V. A., Heilbron, F. C., Niebles, J. C., & Ghanem, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_47 · Cited by 401 · see citing works
Convolutional Two-Stream Network Fusion for Video Action RecognitionPreprint
Feichtenhofer, C., Pinz, A. J., & Zisserman, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.06573 · Cited by 384 · see citing works
Learning Activity Progression in LSTMs for Activity Detection and Early Detection
Ma, S., Sigal, L., & Sclaroff, S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.214 · Cited by 382 · see citing works
The Role of Edge Computing in Internet of Things
Hassan, N., Gillani, S. A., Ahmed, E., Yaqoob, I., & Imran, M. A. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700906 · Cited by 358 · see citing works
Bilal, K., Khalid, O., Erbad, A., & Khan, S. U. (2018) · Computer Networks · doi:10.1016/j.comnet.2017.10.002 · Cited by 334 · see citing works
Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC
Nurvitadhi, E., Sheffield, D., Sim, J., Mishra, A. K., Venkatesh, G., & Marr, D. (2016) · International Conference on Field-Programmable Technology · doi:10.1109/fpt.2016.7929192 · Cited by 323 · see citing works
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Wang, L., Xiong, Y., Wang, Z., et al. (2016) · arXiv · doi:10.48550/arxiv.1608.00859 · Cited by 287 · see citing works
A Deep Normalization and Convolutional Neural Network for Image Smoke Detection
Yin, Z., Wan, B., Yuan, F., Xia, X., & Shi, J. (2017) · IEEE Access · doi:10.1109/access.2017.2747399 · Cited by 285 · see citing works
Learning Spatio-Temporal Representation with Pseudo-3D Residual NetworksPreprint
Qiu, Z., Yao, T., & Mei, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.10305 · Cited by 250 · see citing works
Resource Efficient 3D Convolutional Neural Networks
Köpüklü, O., Köse, N., Gunduz, A., & Rigoll, G. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00240 · Cited by 249 · see citing works
The interpretation of biological motion
Hoffman, D. D., & Flinchbaugh, B. E. (1982) · Biological Cybernetics · doi:10.1007/bf00340076 · Cited by 244 · see citing works
Spatiotemporal Deformable Part Models for Action Detection
Tian, Y., Sukthankar, R., & Shah, M. A. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.341 · Cited by 243 · see citing works
Ke, R., Zhuang, Y., Pu, Z., & Wang, Y. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2984197 · Cited by 240 · see citing works
Face Recognition Using Kernel Ridge Regression
An, S., Liu, W., & Venkatesh, S. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383105 · Cited by 234 · see citing works
Hollywood in Homes: Crowdsourcing Data Collection for Activity UnderstandingPreprint
Sigurdsson, G. A., Varol, G., Wang, X., Farhadi, A., Laptev, I., & Gupta, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.01753 · Cited by 221 · see citing works
Hibernus++: A Self-Calibrating and Adaptive System for Transiently-Powered Embedded Devices
Balsamo, D., Weddell, A. S., Das, A. K., et al. (2016) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2016.2547919 · Cited by 215 · see citing works
Temporal Localization of Actions with Actoms
Gaidon, A., Harchaoui, Z., & Schmid, C. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.65 · Cited by 213 · see citing works
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and BeyondPreprint
Cao, Y., Xu, J., Lin, S. C.-F., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.11492 · Cited by 212 · see citing works
Automated Visual Surveillance in Realistic Scenarios
Shah, M. A., Javed, O., & Shafique, K. (2007) · IEEE Multimedia · doi:10.1109/mmul.2007.3 · Cited by 184 · see citing works
Temporal Action Localization in Untrimmed Videos via Multi-stage CNNsPreprint
Shou, Z., Wang, D., & Chang, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.02129 · Cited by 172 · see citing works
Structured Learning of Human Interactions in TV Shows
Patron-Perez, A., Marszałek, M., Reid, I. D., & Zisserman, A. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.24 · Cited by 171 · see citing works
Analysis of Features for Rigid Structure Vehicle Type Recognition
Petrovic, V. S., & Cootes, T. F. (2004) · British Machine Vision Conference (BMVC) · doi:10.5244/c.18.61 · Cited by 171 · see citing works
BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle Recognition
Sochor, J., Herout, A., & Havel, J. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.328 · Cited by 168 · see citing works
A Survey on an Emerging Area: Deep Learning for Smart City Data
Chen, Q., Wang, W., Wu, F., De, S., Wang, R., Zhang, B., & Huang, X. (2019) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2019.2907718 · Cited by 167 · see citing works
Crowd Density Estimation Using Texture Analysis and Learning
Wu, X., Liang, G., Lee, K. K., & Xu, Y. (2006) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2006.340379 · Cited by 163 · see citing works
Distributed and efficient object detection in edge computing: Challenges and solutions
Ren, J., Guo, Y., Zhang, D., Liu, Q., & Zhang, Y. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700415 · Cited by 148 · see citing works
Temporal Action Detection with Structured Segment NetworksPreprint
Zhao, Y., Xiong, Y., Wang, L., Wu, Z., Tang, X., & Lin, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.06228 · Cited by 123 · see citing works
Age Group and Gender Estimation in the Wild With Deep RoR Architecture
Zhang, K., Gao, C., Guo, L., Sun, M., Yuan, X., Han, T. X., Zhao, Z., & Li, B. (2017) · IEEE Access · doi:10.1109/access.2017.2761849 · Cited by 119 · see citing works
Modeling and analysis of cascading node-link failures in multi-sink wireless sensor networks
Fu, X., & Yang, Y. (2020) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.106815 · Cited by 118 · see citing works
MLP Neural Network Based Gas Classification System on Zynq SoC
Zhai, X., Ali, A. A. S., Amira, A., et al. (2016) · IEEE Access · doi:10.1109/access.2016.2619181 · Cited by 116 · see citing works
Automatic Vehicle Counting from Video for Traffic Flow Analysis
Bas, E., Tekalp, A. M., & Salman, F. S. (2007) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2007.4290146 · Cited by 113 · see citing works
Human Action Recognition using Factorized Spatio-Temporal Convolutional NetworksPreprint
Sun, L., Jia, K., Yeung, D., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.00562 · Cited by 112 · see citing works
Smoke Detection Based on Deep Convolutional Neural Networks
Tao, C., Zhang, J., & Wang, P. (2016) · International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII) · doi:10.1109/iciicii.2016.0045 · Cited by 106 · see citing works
Large Scale Holistic Video Understanding
Diba, A., Fayyaz, M., Sharma, V., Paluri, M., Gall, J., Stiefelhagen, R., & Gool, L. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58558-7_35 · Cited by 106 · see citing works
Indian Movie Face Database: A benchmark for face recognition under wide variations
Gangisetty, S., Husain, M., Beham, M. P., Gudavalli, J., Kandasamy, M., Vaddi, R., Hemadri, V. B., Karure, J. C., Raju, R., Rajan, B., et al. (2013) · National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) · doi:10.1109/ncvpripg.2013.6776225 · Cited by 105 · see citing works
The Disruptions of 5G on Data-Driven Technologies and Applications
Loghin, D., Cai, S., Chen, G., Dinh, T. T. A., Fan, F., Lin, Q., Ng, J., Ooi, B. C., Sun, X., Ta, Q.-T., et al. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2967670 · Cited by 101 · see citing works
3D Parallel Fully Convolutional Networks for Real-Time Video Wildfire Smoke Detection
Li, X., Chen, Z., Wu, Q. M. J., & Liu, C. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2889193 · Cited by 100 · see citing works
An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification
Akbar, M. A., Ali, A. A. S., Amira, A., et al. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2565721 · Cited by 90 · see citing works
Optimizing CNN-Based Object Detection Algorithms on Embedded FPGA Platforms
Zhao, R., Niu, X., Wu, Y., Luk, W. W., & Liu, Q. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-56258-2_22 · Cited by 89 · see citing works
Early Detection System for Gas Leakage and Fire in Smart Home Using Machine Learning
Salhi, L., Silverston, T., Yamazaki, T., & Miyoshi, T. (2019) · International Conference on Consumer Electronics · doi:10.1109/icce.2019.8661990 · Cited by 86 · see citing works
Tracking of Extended Objects with High-Resolution Doppler Radar
Kellner, D., Barjenbruch, M., Klappstein, J., et al. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2501759 · Cited by 75 · see citing works
Internet of things technologies in smart cities
Dlodlo, N., Gcaba, O., & Smith, A. (2016) · IST-Africa Conference (IST-Africa) · doi:10.1109/istafrica.2016.7530575 · Cited by 63 · see citing works
A Video Surveillance System Based on Permissioned Blockchains and Edge Computing
Wang, R., Tsai, W.-T., He, J., Liu, C., Li, Q., & Deng, E. (2019) · 2019 IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp.2019.8679354 · Cited by 61 · see citing works
AReN: A Deep Learning Approach for Sound Event Recognition Using a Brain Inspired Representation
Greco, A., Petkov, N., Saggese, A., et al. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2994740 · Cited by 60 · see citing works
Modeling and analyzing cascading failures for Internet of Things
Fu, X., & Yang, Y. (2020) · Information Sciences · doi:10.1016/j.ins.2020.09.054 · Cited by 60 · see citing works
Towards crowd density-aware video surveillance applications
Fradi, H., & Dugelay, J. (2015) · Information Fusion · doi:10.1016/j.inffus.2014.09.005 · Cited by 55 · see citing works
Vehicle counting and speed measurement using headlight detection
Sina, I., Wibisono, A., Nurhadiyatna, A., et al. (2013) · International Conference on Advanced Computer Science and Information Systems · doi:10.1109/icacsis.2013.6761567 · Cited by 54 · see citing works
Nguyen, D. T., Kim, K., Hong, H. G., et al. (2017) · Sensors · doi:10.3390/s17030637 · Cited by 52 · see citing works
AI-Oriented Large-Scale Video Management for Smart City: Technologies, Standards, and Beyond
Duan, L., Lou, Y., Wang, S., et al. (2018) · IEEE Multimedia · doi:10.1109/mmul.2018.2873564 · Cited by 48 · see citing works
Real-time Vision-based People Counting System for the Security Door
Kim, J., Choi, K.-S., Choi, B.-D., & Ko, S.-J. (2002) · International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) · Cited by 42 · see citing works
Organ, body weight, protein; organ and fat; organ ratios in large albino rats.
Sperling, F. (1953) · PubMed · Cited by 42 · see citing works
Intelligent Multi-Camera Video Surveillance System for Smart City Applications
Alshammari, A., & Rawat, D. B. (2019) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2019.8666579 · Cited by 36 · see citing works
Understanding the potential of human–machine crowdsourcing for weather data
Niforatos, E., Vourvopoulos, A., & Langheinrich, M. (2016) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2016.10.002 · Cited by 35 · see citing works
Multiple vehicle tracking and classification system with a convolutional neural network
Kim, H. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01429-5 · Cited by 34 · see citing works
Gender classification from unconstrained video sequences
Demirkus, M., Toews, M., Clark, J. J., & Arbel, T. (2010) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2010.5543829 · Cited by 33 · see citing works
A wearable human activity recognition system on a chip
Basterretxea, K., Echanobe, J., & del Campo, I. (2014) · Conference on Design and Architectures for Signal and Image Processing · doi:10.1109/dasip.2014.7115600 · Cited by 32 · see citing works
From Open Set to Closed Set: Counting Objects by Spatial Divide-and-ConquerPreprint
Xiong, H., Lü, H., Liu, C., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.06473 · Cited by 32 · see citing works
Autonomous detection and tracking under illumination changes, occlusions and moving camera
Bhaskar, H., Dwivedi, K., Dogra, D. P., Al-Mualla, M. E., & Mihaylova, L. S. (2015) · Signal Processing · doi:10.1016/j.sigpro.2015.06.003 · Cited by 31 · see citing works
Accurate Vehicle Detection Using Multi-camera Data Fusion and Machine Learning
Wu, H., Zhang, X., STORY, B. A., et al. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683350 · Cited by 31 · see citing works
Trajectory-based vehicle tracking at low frame rates
Lee, G., Mallipeddi, R., & Lee, M. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.03.023 · Cited by 28 · see citing works
Fine-grained energy profiling for deep convolutional neural networks on the Jetson TX1
Rodrigues, C. F., Riley, G. D., & Luján, M. (2017) · IEEE International Symposium on Workload Characterization · doi:10.1109/iiswc.2017.8167764 · Cited by 26 · see citing works
Smoke Detection on Video Sequences Using Convolutional and Recurrent Neural Networks
Filonenko, A., Kurnianggoro, L., & Jo, K.-H. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67077-5_54 · Cited by 25 · see citing works
FPGA Implementation of Blue Whale Calls Classifier Using High-Level Programming Tool
Bahoura, M. (2016) · Electronics · doi:10.3390/electronics5010008 · Cited by 24 · see citing works
Holistic Large Scale Video Understanding
Diba, A., Fayyaz, M., Sharma, V., Paluri, M., Gall, J., Stiefelhagen, R., & Gool, L. V. (2019) · Lirias · Cited by 23 · see citing works
Pedestrian Density Analysis in Public Scenes With Spatiotemporal Tensor Features
Chen, K., & Kämäräinen, J. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2516586 · Cited by 22 · see citing works
Security in Modern Smart Cities: An Information Technology Perspective
Jameel, T., Ali, R., & Ali, S. S. (2019) · International Conference on Communication, Computing and Digital Systems (C-CODE) · doi:10.1109/c-code.2019.8681021 · Cited by 22 · see citing works
Generalized Haar Filter-Based Object Detection for Car Sharing Services
Lu, K., Li, J., Zhou, L., Hu, X., An, X., & He, H. (2018) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2018.2830655 · Cited by 21 · see citing works
Age Estimation System Using Deep Residual Network Classification Method
Fariza, A., Mu'arifin, & Arifin, A. Z. (2019) · International Electronics Symposium (IES) · doi:10.1109/elecsym.2019.8901521 · Cited by 18 · see citing works
Acharya, D., Khoshelham, K., & Winter, S. (2017) · RMIT Research Repository (RMIT University Library) · Cited by 18 · see citing works
Spatio-Temporal Motion Detection for Intelligent Surveillance Applications
Al-Berry, M. N., Salem, M. A. ‐., Hussein, A. S., et al. (2014) · International Journal of Computational Methods · doi:10.1142/s0219876213500977 · Cited by 17 · see citing works
Self-citationHuman Detection using HOG Features of Head and Shoulder Based on Depth Map
Tian, Q., Zhou, B., Zhao, W., et al. (2013) · Journal of Software · doi:10.4304/jsw.8.9.2223-2230 · Cited by 15 · see citing works
Water storage and gravity for urban sustainability and climate readiness
Polenghi‐Gross, I., Sabol, S. A., Ritchie, S. R., et al. (2014) · American Water Works Association · doi:10.5942/jawwa.2014.106.0151 · Cited by 15 · see citing works
A smart surveillance system of distributed smart multi cameras modelled as agents
Eigenraam, D., & Rothkrantz, L. J. M. (2016) · doi:10.1109/scsp.2016.7501018 · Cited by 15 · see citing works
Drones in the Smart City and IoT
Sterbenz, J. P. G. (2016) · doi:10.1145/2935620.2949659 · Cited by 15 · see citing works
Lee, K., Yim, K., & Mikki, M. A. (2011) · Computers & Mathematics with Applications · doi:10.1016/j.camwa.2011.08.048 · Cited by 14 · see citing works
Smart City Model: a Literature Review
Achmad, K. A., Nugroho, L. E., Djunaedi, A., et al. (2018) · doi:10.1109/iciteed.2018.8534865 · Cited by 12 · see citing works
Vehicle subtype, make and model classification from side profile video
Boyle, J., & Ferryman, J. M. (2015) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2015.7301783 · Cited by 11 · see citing works
New FPGA architecture for bit-serial pipeline datapath
Ohta, A., Isshiki, T., & Kunieda, H. (2002) · doi:10.1109/fpga.1998.707881 · Cited by 10 · see citing works
Yang, Z., Shi, W., Huang, Z., et al. (2018) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct.2018.8599905 · Cited by 10 · see citing works
Luo, X., Ye, O., & Zhou, B. (2019) · International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI) · doi:10.1109/mlbdbi48998.2019.00026 · Cited by 10 · see citing works
Face recognition using different surveillance cameras
Assaleh, K. T., Shanableh, T., & Abuqaaud, K. A. (2013) · International Conference on Communications, Signal Processing, and their Applications (ICCSPA) · doi:10.1109/iccspa.2013.6487270 · Cited by 8 · see citing works
Vehicle recognition based on Gabor and Log-Gabor transforms
Priyadharshini, R. A., Arivazhagan, S., & Sangeetha, L. (2014) · IEEE International Conference on Advanced Communications, Control and Computing Technologies · doi:10.1109/icaccct.2014.7019303 · Cited by 8 · see citing works
Power Controlled and Stability-based Routing Protocol for Wireless Ad hoc Networks.
Zhang, F., Kang, L., Xu, X., et al. (2017) · Journal of information science and engineering · Cited by 7 · see citing works
Soft Biometrics: Gender Recognition from Unconstrained Face Images Using Local Feature Descriptor
Arigbabu, O. A., Ahmad, S. M. S., Adnan, W. A. W., & Mahmood, S. (2015) · Journal of Information and Communication Technology · doi:10.32890/jict2015.14.7 · Cited by 7 · see citing works
Improvement CNN Performance by Edge Detection Preprocessing for Vehicle Classification Problem
Abdillah, B., Jati, G., & Jatmiko, W. (2018) · International Symposium on Micro-NanoMechatronics and Human Science · doi:10.1109/mhs.2018.8887015 · Cited by 7 · see citing works
MLP on FPGA: Optimal Coding of Data and Activation Function
Alilat, F., & Yahiaoui, R. (2019) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs.2019.8924355 · Cited by 7 · see citing works
Design of an ASIC architecture for high speed fractal image compression
Ancarani, F., Gloria, D. D., Olivieri, M., et al. (2002) · Annual IEEE International ASIC Conference and Exhibit · doi:10.1109/asic.1996.551998 · Cited by 6 · see citing works
MERIT: Tensor Transform for Memory-Efficient Vision Processing on Parallel Architectures
Lin, Y., Chen, W.-C., & Chien, S. (2020) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2019.2953171 · Cited by 6 · see citing works
Artefacts in EEG Signals Epileptic Seizure Prediction using Edge Devices
Natarov, R., Dyka, Z., Bohovyk, R., et al. (2020) · Mediterranean Conference on Embedded Computing (MECO) · doi:10.1109/meco49872.2020.9134076 · Cited by 6 · see citing works
An Open-Source Infrastructure for Real-Time Automatic Agricultural Machine Data Processing
Wang, Y., Balmos, A. D., Layton, A. W., et al. (2017) · 2017 Spokane, Washington July 16 - July 19, 2017 · doi:10.13031/aim.201701022 · Cited by 5 · see citing works
Federated Learning in Smart Cities: A Comprehensive Survey.Preprint
Zheng, Z., Zhou, Y., Sun, Y., et al. (2021) · arXiv (Cornell University) · Cited by 5 · see citing works
Action Recognition for Videos by Long-Term Point Trajectory Analysis with Background Removal
Yu, X., Okada, Y., & Kaneko, K. (2016) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2016.13 · Cited by 4 · see citing works
A new unsupervised model of action recognition
Wang, D., Shao, Q., & Li, X. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7350982 · Cited by 3 · see citing works
SMCA-CNN: Learning a Semantic Mask and Cross-Scale Adaptive Feature for Robust Crowd Counting
Wang, G., Zou, Y. X., Li, Z., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2929675 · Cited by 2 · see citing works
Directional Multi-Scale Stationary Wavelet-Based Representation for Human Action Classification
Al-Berry, M. N., Salem, M. A. ‐., Ebeid, H. M., et al. (2017) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-5225-2229-4.ch014 · Cited by 1 · see citing works
Self-citationMotion coherence based abnormal behavior detection
Xu, Z., Zhu, S., Fu, B., et al. (2017) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2017.7978094 · Cited by 1 · see citing works
Detection of atmospheric emissions by classifying images with convolutional neural networks
Perez-Chust, R., Fernández-Moreno, M., & Garcia, D. F. (2019) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa.2019.8869457 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [109]Zhao H. Yan Z. Wang H. Torresani L. Torralba A. SLAC: A sparsely labeled dataset for action classification and localization arXiv 2017 2 1712.09374
- [125]Avgerinakis K. Briassouli A. Kompatsiaris I. Smoke detection using temporal HOGHOF descriptors and energy colour statistics from video Proceedings of the International Workshop on Multi-Sensor Systems and Networks for Fire Detection and Management Antalya, Turkey 8–9 November 2012
Cited by
Sorted by citations
A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134 · see citing works
“Some survey papers highlight the benefits of using specific datasets in VSSs [8,9].”
Background
- Authors
- Ezzat, M. A., Abd El Ghany, M. A., Almotairi, S., & Salem, M. A.-M.
- Affiliations
- German University in Cairo (EG)
- Electronics Department, German University in Cairo, Cairo 11511, Egypt
- Majmaah University (SA)
- Ain Shams University (EG)
- Corresponding author
- Almotairi, Sultan
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-05-06
- Volume
- 21
- Issue
- 9
- Pages
- 3222
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC8124810
- OpenAlex
- W3161800282
- Cited by
- 39
- References
- 151
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 5 · 2023: 8 · 2022: 8 · 2021: 1
- FWCI
- 2.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Technischen Universität Darmstadt (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- TUbilio (Technical University of Darmstadt) (submittedVersion)
- Technischen Universität Darmstadt (submittedVersion)
- Technischen Universität Darmstadt (cc-by)
- Funders
- Majmaah University
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- video surveillance, smart cities, edge computing, embedded systems, Internet of Things, big data analytics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review