Pedestrian tracking by learning deep features
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Optical flow algorithms are more suitable than frame differencing for dynamically changing environments and can process complex motion patterns.
“The frame differencing and optical flow algorithms both compare consecutive frames to detect changes in pixel values, but optical flow algorithms are more suitable for dynamically changing environments, which can process complex motion patterns [69][70][71].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [70]
Cites
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117 · see citing works
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
(2017) · doi:10.1109/cvpr35066.2017 · Cited by 7,281 · see citing works
Computer vision and pattern recognition
Zheng, N., Loizou, G. D., Jiang, X., Lan, X., & Li, X. (2007) · International Journal of Computer Mathematics · doi:10.1080/00207160701303912 · Cited by 2,463 · see citing works
Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function
Cheng, D., Gong, Y., Zhou, S., Wang, J., & Zheng, N. (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.2016.149 · Cited by 1,288 · see citing works
Cheng, G., Yang, C., Yao, X., Guo, L., & Han, J. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2783902 · Cited by 1,229 · see citing works
Joint Deep Learning for Pedestrian Detection
Ouyang, W., & Wang, X. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.257 · Cited by 665 · see citing works
Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey
Han, J., Zhang, D., Cheng, G., Liu, N., & Xu, D. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2749125 · Cited by 618 · see citing works
Deep Learning Strong Parts for Pedestrian Detection
Tian, Y., Luo, P., Wang, X., & Tang, X. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.221 · Cited by 523 · see citing works
New features and insights for pedestrian detection
Walk, S., Majer, N., Schindler, K., & Schiele, B. (2010) · 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.2010.5540102 · Cited by 468 · see citing works
Unsupervised Cross-Dataset Transfer Learning for Person Re-identification
Peng, P., Xiang, T., Wang, Y., Pontil, M., Gong, S., Huang, T., & Tian, Y. (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.2016.146 · Cited by 432 · see citing works
Filtered channel features for pedestrian detection
Zhang, S., Benenson, R., & Schiele, B. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298784 · Cited by 395 · see citing works
An enhanced deep feature representation for person re-identification
Wu, S., Chen, Y.-C., Li, X., Wu, A.-C., You, J.-J., & Zheng, W.-S. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477681 · Cited by 267 · see citing works
Classifier Fusion With Contextual Reliability Evaluation
Liu, Z., Pan, Q., Dezert, J., Han, J., & He, Y. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2710205 · Cited by 246 · see citing works
Yao, X., Han, J., Zhang, D., & Nie, F. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2694222 · Cited by 230 · see citing works
Robust Object Co-Segmentation Using Background Prior
Han, J., Quan, R., Zhang, D., & Nie, F. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2781424 · Cited by 169 · see citing works
Fusion of Multichannel Local and Global Structural Cues for Photo Aesthetics Evaluation
Zhang, L., Gao, Y., Zimmermann, R., Tian, Q. C., & Li, X. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2303650 · Cited by 168 · see citing works
Weakly Supervised Photo Cropping
Zhang, L., Song, M., Yang, Y., Zhao, Q., Zhao, C., & Sebe, N. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2286817 · Cited by 152 · see citing works
Duplex Metric Learning for Image Set Classification
Cheng, G., Zhou, P., & Han, J. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2760512 · Cited by 130 · see citing works
A Review of Co-Saliency Detection Algorithms
Zhang, D., Fu, H., Han, J., Borji, A., & Li, X. (2018) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3158674 · Cited by 116 · see citing works
Optical flow or image subtraction in human detection from infrared camera on mobile robot
Fernández‐Caballero, A., Castillo, J. C., Martínez-Cantos, J., & Martínez‐Tomás, R. (2010) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2010.06.002 · Cited by 108 · see citing works
Deep Learning of Scene-Specific Classifier for Pedestrian Detection
Zeng, X., Ouyang, W., Wang, M., & Wang, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10578-9_31 · Cited by 104 · see citing works
A motion and shape-based pedestrian detection algorithm
El-Zein, H., Lakshmanan, S., & Watta, P. B. (2004) · doi:10.1109/ivs.2003.1212962 · Cited by 83 · see citing works
2018 International Conference on Indoor Positioning and Indoor Navigation
(2018) · International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin.2018.8533687 · Cited by 83 · see citing works
Kwak, J.-Y., Ko, B. C., & Nam, J. Y. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2569159 · Cited by 60 · see citing works
Face recognition with a small occluded training set using spatial and statistical pooling
Long, Y., Zhu, F., Shao, L., & Han, J. (2017) · Information Sciences · doi:10.1016/j.ins.2017.10.042 · Cited by 51 · see citing works
Antigny, N., Servieres, M. C. J., & Renaudin, V. (2017) · International Conference on Indoor Positioning and Indoor Navigation · doi:10.1109/ipin.2017.8115934 · Cited by 15 · see citing works
Applications of Computer Vision
Kumar, L. A., & Renuka, D. K. (2023) · CRC Press eBooks · doi:10.1201/9781003348689-10 · Cited by 8 · see citing works
Flagged first, then by citations
2 of the 29 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Intelligent video surveillance: A review through deep learning techniques for crowd analysis
Sreenu, G., & Saleem Durai, M. A. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0212-5 · Cited by 460 · see citing works
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
Deep learning support for intelligent transportation systems
Guerrero‐Ibañez, J., Contreras-Castillo, J. J., & Zeadally, S. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4169 · Cited by 80 · see citing works
Zhou, Y., Wu, C., Wu, Q., Eli, Z. M., Xiong, N. N., & Sheng, Z. (2019) · Electronics · doi:10.3390/electronics8121504 · Cited by 8 · see citing works
CRIME PREDICTION BASED ON PERSON-WEAPONS RELATION USING DEEP LEARNING TECHNIQUES
(2023) · European Chemical Bulletin · doi:10.48047/ecb/2023.12.si5.0118 · Cited by 8 · see citing works
Li, C., Wang, Y., & Liu, X. (2022) · Applied Sciences · doi:10.3390/app12199597 · Cited by 6 · see citing works
Hasware, D. U. A. (2023) · Proceeding International Conference on Science and Engineering · doi:10.52783/cienceng.v11i1.369 · Cited by 2 · see citing works
Smart Surveillance: A Review & Survey Through Deep Learning Techniques for Detection & Analysis
(2023) · Journal of Sensor Networks and Data Communications · doi:10.33140/jsndc.03.01.05 · Cited by 1 · see citing works
(2023) · International Scientific Journal Monte · doi:10.33807/monte.2023v7n2
Use of video analytics in security systems for crime prevention and evidence collection
Leka, A., & Ndoj, E. (2023) · International Scientific Journal Monte · doi:10.33807/monte.20232834
Deep Surveillance SystemPreprint
Pritam, Mushuni, S. S., Joshi, H. N., & Goel, S. S. (2023) · Research Square · doi:10.21203/rs.3.rs-3049151/v1
(2023) · Journal of Sensor Networks and Data Communications · doi:10.33140/jsndc.03.01.04
A Survey on Crowd Analysis Using Artificial Intelligence TechniquesPreprint
Abdelwahab, K. M., Shoaib, M. R., El‐Samie, F. E. A., & Khalaf, A. A. M. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4608467
Real Time Stampede Detection System Using Computer VisionPreprint
Verma, U., & Mittal, U. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4490376
Optical flow techniques for crowd behavior analysis
Abdelwahab, K. M., El‐Samie, F. E. A., Khalaf, A. A. M., El-Sawy, A. M., & Shalaby, W. A. (2026) · Journal of Optics · doi:10.1007/s12596-025-02938-7
- Authors
- Huang, H., Xu, Y., Huang, Y., Yang, Q., & Zhou, Z.
- Affiliations
- State Grid Corporation of China (China) (CN)
- Corresponding author
- Huang, Honghe
- Venue
- Journal of Visual Communication and Image Representation
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 57
- Pages
- 172-175
- ISSN
- 1047-3203 (ISSN-L)
- OpenAlex
- W2899001800
- Cited by
- 15
- References
- 29
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 9 · 2022: 1 · 2020: 1 · 2019: 2
- FWCI
- 0.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Visual Communication and Image Representation (publishedVersion)
- Topics
- 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
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- pedestrian tracking, deep features, convolutional neural networks, optical flow, video surveillance, pedestrian detection, illumination robustness
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