EdgeEye: An Edge Service Framework for Real-time Intelligent Video Analytics
Open accessAbstract
Deep learning with Deep Neural Networks (DNNs) can achieve much higher accuracy on many computer vision tasks than classic machine learning algorithms. Because of the high demand for both computation and storage resources, DNNs are often deployed in the cloud. Unfortunately, executing deep learning inference in the cloud, especially for real-time video analysis, often incurs high bandwidth consumption, high latency, reliability issues, and privacy concerns. Moving the DNNs close to the data source with an edge computing paradigm is a good approach to address those problems. The lack of an open source framework with a high-level API also complicates the deployment of deep learning-enabled service at the Internet edge. This paper presents EdgeEye, an edge-computing framework for real-time intelligent video analytics applications. EdgeEye provides a high-level, task-specific API for developers so that they can focus solely on application logic. EdgeEye does so by enabling developers to transform models trained with popular deep learning frameworks to deployable components with minimal effort. It leverages the optimized inference engines from industry to achieve the optimized inference performance and efficiency.
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EdgeEye presents an edge service framework that realizes a high-level abstraction of real-time video analytic functions based on DL.
“Besides, in order to optimize performance and efficiency, [108] presents an edge service framework, i.e., EdgeEye, which realizes a high-level abstraction of real-time video analytic functions based on DL.”
From Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2020) · cites this paper as [108]
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Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
(2016) · Cited by 31,590 · see citing works
SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128 · see citing works
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,369 · see citing works
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., & Zhang, Z. (2015) · arXiv · doi:10.48550/arxiv.1512.01274 · Cited by 1,819 · see citing works
Real-Time Video Analytics: The Killer App for Edge Computing
Ananthanarayanan, G., Bahl, P., Bodik, P., Chintalapudi, K., Philipose, M., Ravindranath, L., & Sinha, S. (2017) · Computer · doi:10.1109/mc.2017.3641638 · Cited by 496 · see citing works
Torch: a modular machine learning software library
Collobert, R., Bengio, S., & Mariéthoz, J. (2002) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 443 · see citing works
(2017) · Networked Systems Design and Implementation · Cited by 329 · see citing works
The Design and Implementation of a Wireless Video Surveillance System
Zhang, T., Chowdhery, A., Bahl, P., Jamieson, K., & Banerjee, S. (2015) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/2789168.2790123 · Cited by 275 · see citing works
Self-citationParaDrop: Enabling Lightweight Multi-tenancy at the Network’s Extreme Edge
Liu, P., Willis, D., & Banerjee, S. (2016) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec.2016.39 · Cited by 115 · see citing works
Self-citationFernández, L. L., París, M., Mejias, R. B., Lopez, F. J., & Arnaiz, J. A. S. (2013) · IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) · doi:10.1109/wowmom.2013.6583507 · Cited by 32 · see citing works
User-centric Composable Services: A New Generation of Personal Data AnalyticsPreprint
Zhao, J., Mortier, R., Crowcroft, J., & Wang, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.09027
Flagged first, then by citations
Cited by
Sorted by citations
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2970550 · Cited by 1,498 · see citing works
“Besides, in order to optimize performance and efficiency, [108] presents an edge service framework, i.”
BackgroundAll one needs to know about fog computing and related edge computing paradigms: A complete survey
Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., & Jue, J. P. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.02.009 · Cited by 1,368 · see citing works
Edge Intelligence: Empowering Intelligence to the Edge of Network
Xu, D., Li, T., Li, Y., Su, X. J., Tarkoma, S., Jiang, T., Crowcroft, J., & Hui, P. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3119950 · Cited by 281 · see citing works
Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models
Wang, X., Tang, Z., Guo, J., Meng, T., Wang, C., Wang, T., & Weijia Jia, B. (2025) · ACM Computing Surveys · doi:10.1145/3724420 · Cited by 230 · see citing works
Edge Intelligence: Architectures, Challenges, and ApplicationsPreprint
Xu, D., Li, T., Li, Y., Su, X., Tarkoma, S., Jiang, T., Crowcroft, J., & Hui, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12172 · Cited by 184 · see citing works
Dhakal, A., Kulkarni, S. G., & Ramakrishnan, K. K. (2020) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3419111.3421284 · Cited by 127 · see citing works
Badidi, E., Moumane, K., & Ghazi, F. E. (2023) · IEEE Access · doi:10.1109/access.2023.3300658 · Cited by 115 · see citing works
A Survey on Collaborative DNN Inference for Edge Intelligence
Ren, W., Qu, Y., Dong, C., Jing, Y., Sun, H., Wu, Q., & Guo, S. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1391-7 · Cited by 107 · see citing works
PMC: A Privacy-preserving Deep Learning Model Customization Framework for Edge Computing
Liu, B., Li, Y., Liu, Y., Guo, Y., & Chen, X. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3432208 · Cited by 95 · see citing works
RES: Real-Time Video Stream Analytics Using Edge Enhanced Clouds
Ali, M. K., Anjum, A., RANA, O. F., Zamani, A. R., Balouek‐Thomert, D., & Parashar, M. (2020) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2020.2991748 · Cited by 73 · see citing works
Cloud Versus Edge Deployment Strategies of Real-Time Face Recognition Inference
Koubaa, A., Ammar, A., Kanhouch, A., & AlHabashi, Y. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3055835 · Cited by 69 · see citing works
What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges
Gedeon, J., Brandherm, F., Egert, R., Grube, T., & Mühlhäuser, M. (2019) · IEEE Access · doi:10.1109/access.2019.2948399 · Cited by 68 · see citing works
Approximate query service on autonomous IoT cameras
Xu, M., Zhang, X., Liu, Y., Huang, G., Liu, X., & Lin, F. X. (2020) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3386901.3388948 · Cited by 48 · see citing works
Optimizing Edge AI: A Comprehensive Survey on Data, Model, and System StrategiesPreprint
Wang, X., & Jia, W. (2025) · Qeios · doi:10.32388/izohch · Cited by 42 · see citing works
Semi-supervised Federated Learning for Activity RecognitionPreprint
Zhao, Y., Liu, H., Li, H., Barnaghi, P. M., & Haddadi, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.00851 · Cited by 51 · see citing works
Distributed resource scheduling in edge computing: Problems, solutions, and opportunities
Sahni, Y., Cao, J., Yang, L., & Wang, S. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109430 · Cited by 38 · see citing works
PArtNNer: Platform-Agnostic Adaptive Edge-Cloud DNN Partitioning for Minimizing End-to-End Latency
Ghosh, S. K., Raha, A., Raghunathan, V., & Raghunathan, A. (2023) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3630266 · Cited by 35 · see citing works
mVideo: Edge Computing Based Mobile Video Processing Systems
Sun, H., Yu, Y., Sha, K., & Lou, B. (2019) · IEEE Access · doi:10.1109/access.2019.2963159 · Cited by 32 · see citing works
Increasing Traffic Safety with Real-Time Edge Analytics and 5G
Lujic, I., Maio, V. D., Pollhammer, K., Bodrozic, I., Lasic, J., & Brandić, I. (2021) · International Workshop on Edge Systems, Analytics and Networking (EdgeSys) · doi:10.1145/3434770.3459732 · Cited by 31 · see citing works
Manzoor, S., Kim, E., Joo, S.-H., Bae, S.-H., In, G.-G., Joo, K.-J., Choi, J.-H., & Kuc, T. (2022) · IEEE Access · doi:10.1109/access.2022.3190538 · Cited by 24 · see citing works
Ravindran, A. (2023) · IoT · doi:10.3390/iot4040021 · Cited by 27 · see citing works
Privacy-preserving activity and health monitoring on databox
Zhao, Y., Haddadi, H., Skillman, S., Enshaeifar, S., & Barnaghi, P. M. (2020) · doi:10.1145/3378679.3394529 · Cited by 24 · see citing works
Yadav, P., Salwala, D., & Curry, E. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3075336 · Cited by 23 · see citing works
Yousefpour, A., Devic, S., Nguyen, B., Kreidieh, A., Liao, A., Bayen, A. M., & Jue, J. P. (2019) · International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things (AIChallengeIoT) · doi:10.1145/3363347.3363366 · Cited by 23 · see citing works
A Decade of Video Analytics at Edge: Training, Deployment, Orchestration, and Platforms
Bai, T., Zhao, H., Huang, L., Wang, Z., Kim, D. I., & Nallanathan, A. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3563377 · Cited by 22 · see citing works
A Scalable Platform for Distributed Object Tracking Across a Many-Camera Network
Khochare, A., Krishnan, A., & Simmhan, Y. L. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3049450 · Cited by 21 · see citing works
Salehe, M., Hu, Z., Mortazavi, S. H., Mohomed, I., & Capes, T. (2019) · International Middleware Conference · doi:10.1145/3366626.3368131 · Cited by 21 · see citing works
PacketGame: Multi-Stream Packet Gating for Concurrent Video Inference at Scale
Yuan, M., Zhang, L., You, X., & Li, X. (2023) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/3603269.3604825 · Cited by 20 · see citing works
Vision-Based Module for Herding with a Sheepdog Robot
del Castillo, V. R., Sánchez-González, L., Campazas, A., & Strisciuglio, N. (2022) · Sensors · doi:10.3390/s22145321 · Cited by 19 · see citing works
Automated Traffic Volume Analytics at Road Intersections Using Computer Vision Techniques
Qi, B., Zhao, W., Zhang, H., Jin, Z., Wang, X., & Runge, T. M. (2019) · International Conference on Transportation Information and Safety · doi:10.1109/ictis.2019.8883683 · Cited by 19 · see citing works
Self-citationVivid: Augmenting Vision-Based Indoor Navigation System With Edge Computing
Zhao, W., Xu, L., Qi, B., Hu, J. S., Wang, T., & Runge, T. M. (2020) · IEEE Access · doi:10.1109/access.2020.2978123 · Cited by 19 · see citing works
Self-citationMatching-based Scheduling of Asynchronous Data Processing Workflows on the Computing Continuum
Mehran, N., Samani, Z. N., Kimovski, D., & Prodan, R. (2022) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/cluster51413.2022.00021 · Cited by 18 · see citing works
Automating Deep Neural Network Model Selection for Edge Inference
Lu, B., Yang, J., Chen, L. Y., & Ren, S. (2019) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi48466.2019.00035 · Cited by 18 · see citing works
Optimizing Face Recognition Inference with a Collaborative Edge–Cloud Network
Oroceo, P. P., Kim, J., Caliwag, E. M. F., Kim, S., & Lim, W. (2022) · Sensors · doi:10.3390/s22218371 · Cited by 17 · see citing works
Towards Performance Clarity of Edge Video AnalyticsPreprint
Xiao, Z., Xia, Z., Zheng, H.-T., Zhao, B. Y., & Jiang, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.08694 · Cited by 14 · see citing works
DACAPO: Accelerating Continuous Learning in Autonomous Systems for Video Analytics
Kim, Y., Oh, C., Hwang, J., Kim, W., Oh, S., Lee, Y., Sharma, H., Yazdanbakhsh, A., & Park, J. (2024) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca59077.2024.00093 · Cited by 16 · see citing works
A Low-cost Simultaneous Localization And Mapping Algorithm For Last-mile Indoor Delivery
Wang, W.-M., Zhao, W., Wang, X., Jin, Z.-H., Li, Y., & Runge, T. (2019) · International Conference on Transportation Information and Safety · doi:10.1109/ictis.2019.8883749 · Cited by 16 · see citing works
GPU-based embedded edge server configuration and offloading for a neural network service
Kim, J., Ullah, S., & Kim, D. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-03623-9 · Cited by 13 · see citing works
SEA-LEAP: Self-Adaptive and Locality-Aware Edge Analytics Placement
Lujic, I., Maio, V. D., Venugopal, S., & Brandić, I. (2021) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2021.3104458 · Cited by 13 · see citing works
Flexible Deployment of Machine Learning Inference Pipelines in the Cloud–Edge–IoT Continuum
Bogacka, K., Sowiński, P., Danilenka, A., Biot, F. M., Wasielewska-Michniewska, K., Ganzha, M., Paprzycki, M., & Palau, C. E. (2024) · Electronics · doi:10.3390/electronics13101888 · Cited by 13 · see citing works
iGenEdge: Intelligent Generative AI Service Deployment for Edge-Connected IoT Devices
Akram, F., Malik, A. W., & Khan, S. U. (2025) · IEEE Internet Computing · doi:10.1109/mic.2025.3576072 · Cited by 12 · see citing works
Collaborative Intelligent Cross-Camera Video Analytics at Edge
Pasandi, H. B., & Nadeem, T. (2019) · International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things (AIChallengeIoT) · doi:10.1145/3363347.3363360 · Cited by 12 · see citing works
ESMO: Joint Frame Scheduling and Model Caching for Edge Video Analytics
Li, T., Sun, J., Liu, Y., Zhang, X., Zhu, D., Guo, Z., & Geng, L. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3281598 · Cited by 10 · see citing works
Camera-Based Edge Analytics for Drilling Optimization
Gooneratne, C. P., Magana-Mora, A., Affleck, M., Otalvora, W. B. C., Zhan, G. D., & Moellendick, T. E. (2020) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/edge50951.2020.00024 · Cited by 10 · see citing works
Transparent AR Processing Acceleration at the Edge
Trinelli, M., Gallo, M., Rifai, M., & Pianese, F. (2019) · International Workshop on Edge Systems, Analytics and Networking (EdgeSys) · doi:10.1145/3301418.3313942 · Cited by 9 · see citing works
EdgeEye: A Data-Driven Approach for Optimal Deployment of Edge Video Analytics
Sun, H., Yu, Y., Sha, K.-W., & Zhong, H. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3166896 · Cited by 9 · see citing works
ResiliNet: Failure-Resilient Inference in Distributed Neural NetworksPreprint
Yousefpour, A., Nguyen, B., Devic, S., Wang, G., Kreidieh, A., Löbel, H., Bayen, A. M., & Jue, J. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.07386 · Cited by 9 · see citing works
Containerized Computer Vision Applications on Edge Devices
Alqaisi, O. I., Tosun, A. Ş., & Korkmaz, T. (2023) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/edge60047.2023.00014 · Cited by 8 · see citing works
Aggressive Lane-change Analysis Closing to Intersection Based on UAV Video and Deep Learning
Hao, Y., Xu, L., Wang, X., Li, Y., & Chen, G. (2019) · International Conference on Transportation Information and Safety · doi:10.1109/ictis.2019.8883543 · Cited by 8 · see citing works
Danish, M., Brazauskas, J., Bricheno, R., Lewis, I., & Mortier, R. (2020) · doi:10.1145/3378679.3394535 · Cited by 10 · see citing works
Sassu, A., Saenz-Cogollo, J. F., & Agelli, M. (2021) · Sensors · doi:10.3390/s21124045 · Cited by 8 · see citing works
Qi, B., Zhao, W., Wang, X., Li, S., & Runge, T. M. (2019) · International Conference on Transportation Information and Safety · doi:10.1109/ictis.2019.8883750 · Cited by 7 · see citing works
Self-citationWixor: Dynamic TDD Policy Adaptation for 5G/xG Networks
Hassan, A., Aggarwal, S., Ibrahim, M., Sharma, P., & Qian, F. (2024) · Proceedings of the ACM on Networking · doi:10.1145/3696395 · Cited by 6 · see citing works
Wang, T., Xu, L., Chen, G., & Zhao, W. (2019) · International Conference on Transportation Information and Safety · doi:10.1109/ictis.2019.8883852 · Cited by 6 · see citing works
Towards a Methodology for Building Dynamic Urgent Applications on Continuum Computing Platforms
Balouek‐Thomert, D., Caron, E., Lefèvre, L., & Parashar, M. (2022) · doi:10.1109/ciw-ius56691.2022.00009 · Cited by 5 · see citing works
Edge Computing for Artificial Intelligence
Wang, X., Han, Y., Leung, V. C. M., Niyato, D. T., Yan, X., & Chen, X. (2020) · Springer eBooks · doi:10.1007/978-981-15-6186-3_7 · Cited by 5 · see citing works
Lachner, C., Mann, Z. Á., & Dustdar, S. (2021) · IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW) · doi:10.1109/icdcsw53096.2021.00009 · Cited by 5 · see citing works
Adaptive ResNet Architecture for Distributed Inference in Resource-Constrained IoT Systems
Khan, F. M., Baccour, E., Erbad, A., & Hamdi, M. (2023) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc58020.2023.10182881 · Cited by 5 · see citing works
An Intelligent Doorbell Design Using Federated Deep Learning
Patel, V., Kanani, S., Pathak, T., Patel, P., Ali, M. I., & Breslin, J. G. (2020) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3430984.3430988 · Cited by 4 · see citing works
Partial Computation Offloading and Resource Allocation via Deep Deterministic Policy Gradient
Shan, Y., Liao, P., Wang, Z., & An, L. (2022) · International Conference on Networking and Network Applications (NaNA) · doi:10.1109/nana56854.2022.00070 · Cited by 4 · see citing works
HEFTLess: A Bi-Objective Serverless Workflow Batch Orchestration on the Computing Continuum
Farahani, R., Mehran, N., Ristov, S., & Prodan, R. (2024) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/cluster59578.2024.00032 · Cited by 4 · see citing works
Analytics-Aware Storage of Surveillance Videos: Implementation and Optimization
Tsai, M., Venkatasubramanian, N., & Hsu, C.-H. (2020) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp50058.2020.00024 · Cited by 4 · see citing works
SOCA-DOM: A Mobile System-on-Chip Array System for Analyzing Big Data on the Move
Li, L., Liu, J.-Y., Fan, J.-P., Qian, X., Hwang, K., Chung, Y., & Yu, Z. (2022) · Journal of Computer Science and Technology · doi:10.1007/s11390-022-1087-z · Cited by 4 · see citing works
Rev: A Video Engine for Object Re-identification at the City Scale
Xu, T., Shen, K., Fu, Y. L., Shi, H., & Lin, F. X. (2022) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec54971.2022.00022 · Cited by 4 · see citing works
TAPU: A Transmission-Analytics Processing Unit for Accelerating Multifunctions in IoT Gateways
Liang, H., Sang, Q., Hu, C., Gong, Y., Cheng, D., Zhou, X., & Wang, Y. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3279892 · Cited by 4 · see citing works
Failout: Achieving Failure-Resilient Inference in Distributed Neural Networks.Preprint
Yousefpour, A., Nguyen, B., Devic, S., Wang, G., Kreidieh, A., Löbel, H., Bayen, A. M., & Jue, J. P. (2020) · arXiv (Cornell University) · Cited by 4 · see citing works
Multi-level feature driven storage management of surveillance videos
Tsai, M.-H., Venkatasubramanian, N., & Hsu, C.-H. (2021) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2021.101441 · Cited by 4 · see citing works
Technical and Economic Feasibility Assessment of a Cloud-Enabled Traffic Video Analysis Framework
Huang, T., & Sharma, A. (2020) · Journal of Big Data Analytics in Transportation · doi:10.1007/s42421-020-00027-8 · Cited by 3 · see citing works
Privacy-Preserving Edge Video Analytics
Hu, M., Fu, Y., & Wu, D. (2020) · Advances in information security · doi:10.1007/978-3-030-57328-7_7 · Cited by 3 · see citing works
A Demonstration of Smart Doorbell Design Using Federated Deep LearningPreprint
Patel, V., Kanani, S., Pathak, T., Patel, P., Ali, M. I., & Breslin, J. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.09687 · Cited by 3 · see citing works
Artificial Intelligence Applications on Edge
Wang, X., Han, Y., Leung, V. C. M., Niyato, D. T., Yan, X., & Chen, X. (2020) · Springer eBooks · doi:10.1007/978-981-15-6186-3_4 · Cited by 2 · see citing works
EdgeWare: toward extensible and flexible middleware for connected vehicle services
Lu, S., Yao, Y., Luo, B., Yu, Z., Li, D., & Shi, W. (2022) · CCF Transactions on High Performance Computing · doi:10.1007/s42514-022-00100-4 · Cited by 2 · see citing works
ST-360: Spatial–Temporal Filtering-Based Low-Latency 360-Degree Video Analytics Framework
Li, J., Liao, J., Chen, B., Nguyen, A., Tiwari, A., Zhou, Q., Yan, Z., & Nahrstedt, K. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3694685 · Cited by 2 · see citing works
Indoor Navigation for a Complex Parking Building Based on Computer Vision
Jia, Y., Jin, Z., Zhang, H., Li, Y., Wang, X., & Shen, L. (2019) · International Conference on Transportation Information and Safety · doi:10.1109/ictis.2019.8883716 · Cited by 2 · see citing works
A Data Protection Focused Adaptation Engine for Distributed Video Analytics Pipelines
Lachner, C., Laufer, J., Dustdar, S., & Pohl, K. (2022) · IEEE Access · doi:10.1109/access.2022.3185990 · Cited by 2 · see citing works
Towards Edge-Cloud Architectures for Personal Protective Equipment Detection
Legierski, J., Rachwał, K., Sowiński, P., Niewolski, W., Ratuszek, P., Kopertowski, Z., Paprzycki, M., & Ganzha, M. (2022) · International Conference on Information Management & Machine Intelligence (ICIMMI) · doi:10.1145/3590837.3590921 · Cited by 2 · see citing works
Jain, K., Adapa, K. S., Grover, K. S. S., Sarvadevabhatla, R. K., & Purini, S. (2023) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid57682.2023.00054 · Cited by 2 · see citing works
Video Analytics Architecture with Metadata Event-Engine for Urban Safe Cities
de Gauna, D. E. R., Irigoyen, E., Cejudo, I., Arregui, H., Leškovský, P., & Otaegui, O. (2021) · International Conference on Computer Technology Applications (ICCTA) · doi:10.1145/3477911.3477919 · Cited by 2 · see citing works
Genomic Data Analysis With Optimized Convolutional Neural Network (CNN) for Edge Applications
Venkateshalu, S., Deshpande, S. L., & Pannyagol, B. (2023) · doi:10.1002/9781394213726.ch6 · Cited by 2 · see citing works
FrameFeedback: A Closed-Loop Control System for Dynamic Offloading Real-Time Edge Inference
Jackson, M., Ji, B., & Nikolopoulos, D. S. (2024) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw63119.2024.00116 · Cited by 1 · see citing works
A Contributory Public-Event Recording and Querying System
Joseph, A., Yadav, N., Ganapathy, V., & Behl, D. (2023) · ACM/IEEE Symposium on Edge Computing (SEC) · doi:10.1145/3583740.3628445 · Cited by 1 · see citing works
Resilience of Entropy Model in Distributed Neural Networks
Zhang, M., Abdi, M., Rifat, S., & Restuccia, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72664-4_24 · Cited by 1 · see citing works
OVIDA: Orchestrator for Video Analytics on Disaggregated Architecture
Singh, M., Rachuri, S. P., Cao, B. B., Sharma, A., Bhumireddy, V., Bronzino, F., Das, S. R., Gandhi, A., & Jain, S. (2024) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec62691.2024.00019 · Cited by 1 · see citing works
Prioritize Winter Crash Severity Influencing Factors in US Midwestern for Autonomous Vehicle
Dai, S. (2020) · Minds at UW (University of Wisconsin) · Cited by 1 · see citing works
Approximate Query Processing on Autonomous Cameras.
Xu, M., Zhang, X., Liu, Y., Liu, X., & Lin, F. X. (2019)
A Distributed Learning Simulation Platform for Edge Hierarchies
Bhushan, A., Shirke, A., Lahoti, G., & Bellur, U. (2020) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets48256.2020.9027386
Clique: Spatiotemporal Object Re-identification at the City ScalePreprint
Xu, T., Shen, K., Fu, Y. L., Shi, H., & Lin, F. X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.09329
Hierarchical Data Storage And Processing on the Edge of the Network
Mortazavi, S. H. (2020) · TSpace (University of Toronto)
Imperio: Cyber Foraging Framework for On-Device Inference based Mobile Applications
Wijayasekara, V. A., & Vekneswaran, P. (2021) · International Conference on Information and Automation for Sustainability (ICIAfS) · doi:10.1109/iciafs52090.2021.9605965
COVID bell – A smart doorbell solution for prevention of COVID-19
Gudipalli, A., Kejriwal, V., Patel, V., Gupta, R., & Dixit, U. (2023) · Paladyn Journal of Behavioral Robotics · doi:10.1515/pjbr-2022-0115
QueryEdge: Real-Time Muti-Video Query in Edge-Cloud Collaborative System
Zhong, J., Niu, Y., & Zhu, M. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10393960
SoK: Evaluation of Methods for Privacy Preserving Edge Video Analytics
Joseph, A., & Ganapathy, V. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-13714-2_24
Platforms for Edge Video Analytics
Bai, T. (2025) · Textbooks in telecommunication engineering · doi:10.1007/978-3-032-14764-6_7
- Authors
- Liu, P., Qi, B., & Banerjee, S.
- Affiliations
- University of Wisconsin–Madison (US)
- Venue
- Proceedings of the 1st International Workshop on Edge Systems, Analytics and Networking
- Publisher
- ACM
- Published
- 2018-06-10
- Pages
- 1-6
- OpenAlex
- W2807214292
- Cited by
- 93
- References
- 15
- Citations by year
- 2025: 6 · 2024: 8 · 2023: 14 · 2022: 13 · 2021: 13 · 2020: 24 · 2019: 15
- FWCI
- 10.75
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Workshop on Edge Systems, Analytics and Networking (EdgeSys) (publishedVersion)
- Funders
- National Science Foundation (CNS-1345293, CNS-14055667, CNS-1525586, CNS-1555426, CNS-1629833, CNS-1647152, CNS-1719336)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- edge computing, deep neural network, real-time video processing, inference optimization, model deployment, open source software framework, privacy
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