High-Speed tracking-by-detection without using image information
Abstract
Tracking-by-detection is a common approach to multi-object tracking. With ever increasing performances of object detectors, the basis for a tracker becomes much more reliable. In combination with commonly higher frame rates, this poses a shift in the challenges for a successful tracker. That shift enables the deployment of much simpler tracking algorithms which can compete with more sophisticated approaches at a fraction of the computational cost. We present such an algorithm and show with thorough experiments its potential using a wide range of object detectors. The proposed method can easily run at 100K fps while outperforming the state-of-the-art on the DETRAC vehicle tracking dataset.
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CSRNet density map estimation has been proven for vehicles as well as people.
“In addition to these studies, counting of objects in crowd is implemented using DL based Congested Scene Recognition (CSR) concept, also referred as density map estimation, in [123] which is not only applied on people but have been proven for vehicles too.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [123]
Cites
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Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
Object Detection with Discriminatively Trained Part-Based Models
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MOT16: A Benchmark for Multi-Object Tracking
Milan, A., Leal-Taixe, L., Reid, I., Roth, S., & Schindler, K. (2016) · arXiv · doi:10.48550/arxiv.1603.00831 · Cited by 1,309 · see citing works
Globally-optimal greedy algorithms for tracking a variable number of objects
Pirsiavash, H., Ramanan, D., & Fowlkes, C. C. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995604 · Cited by 790 · see citing works
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
Cai, Z., Saberian, M., & Vasconcelos, N. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.384 · Cited by 326 · see citing works
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FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking
Zhang, Y., Wang, C., Wang, X., Zeng, W., & Liu, W. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01513-4 · Cited by 1,861 · see citing works
Detection and tracking meet drones challenge
Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., & Ling, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3119563 · Cited by 1,336 · see citing works
StrongSORT: Make DeepSORT Great Again
Du, Y., Zhao, Z., Song, Y., Zhao, Y., Su, F., Gong, T., & Meng, H. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3240881 · Cited by 1,191 · see citing works
“V-IOUTracker [5] extends IOUTracker [4] by falling back to single-object tracking while missing detection occurs.”
BackgroundMethodThe Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking
Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., Zhang, W., Huang, Q., & Tian, Q. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01249-6_23 · Cited by 1,099 · see citing works
UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking
Wen, L., Du, D., Cai, Z., Lei, Z., Chang, M.-C., Qi, H., Lim, J., Yang, M.-H., & Lyu, S. (2020) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2020.102907 · Cited by 804 · see citing works
Quasi-Dense Similarity Learning for Multiple Object Tracking
Pang, J., Qiu, L., Li, X., Chen, H., Li, Q., Darrell, T., & Yu, F. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00023 · Cited by 480 · see citing works
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
Dendorfer, P., Os̆ep, A., Milan, A., Schindler, K., Cremers, D., Reid, I., Roth, S., & Leal-Taixé, L. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01393-0 · Cited by 374 · see citing works
A Systematic Review of Drone Based Road Traffic Monitoring System
Bisio, I., Garibotto, C., Haleem, H., Lavagetto, F., & Sciarrone, A. (2022) · IEEE Access · doi:10.1109/access.2022.3207282 · Cited by 121 · see citing works
“Various approaches to object tracking have been proposed 932 in the literature, such as Intersection over Union (IoU)- 933 based tracking [123], Simple Online and Real-Time Tracking 934 vehicles.”
MethodVehicle Tracking Using Deep SORT with Low Confidence Track Filtering
Hou, X., Wang, Y., & Chau, L.-P. (2019) · 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2019.8909903 · Cited by 123 · see citing works
Neupane, B., Horanont, T., & Aryal, J. (2022) · Sensors · doi:10.3390/s22103813 · Cited by 85 · see citing works
“[33] tested several DL models to produce detection and used a passive detection filter for MOT, producing results in one of the fastest tracking methods.”
MethodKalake, L., Wan, W., & Hou, L. (2021) · IEEE Access · doi:10.1109/access.2021.3060821 · Cited by 68 · see citing works
An Adaptive Framework for Multi-Vehicle Ground Speed Estimation in Airborne Videos
Li, J., Chen, S., Zhang, F., Li, E., Yang, T., & Lu, Z. (2019) · Remote Sensing · doi:10.3390/rs11101241 · Cited by 57 · see citing works
Online multi-object tracking with pedestrian re-identification and occlusion processing
Zhang, X., Wang, X., & Gu, C. (2021) · The Visual Computer · doi:10.1007/s00371-020-01854-0 · Cited by 35 · see citing works
Vehicle Detection and Tracking from Surveillance Cameras in Urban Scenes
Messoussi, O., de Magalhães, F. G., Lamarre, F., Perreault, F., Sogoba, I., Bilodeau, G.-A., & Nicolescu, G. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-90436-4_15 · Cited by 9 · see citing works
- Authors
- Bochinski, E., Eiselein, V., & Sikora, T.
- Affiliations
- Technische Universität Berlin (DE)
- ORCID
- Sikora, Thomas
- Venue
- 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
- Publisher
- IEEE
- Published
- 2017-08
- Pages
- 1-6
- OpenAlex
- W2766984662
- Cited by
- 707
- References
- 5
- Citations by year
- 2026: 24 · 2025: 63 · 2024: 95 · 2023: 109 · 2022: 124 · 2021: 104 · 2020: 93 · 2019: 59 · 2018: 35 · 2017: 1
- FWCI
- 15.03
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (publishedVersion)
- Funders
- Bundesministerium für Bildung und Forschung
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- tracking-by-detection, multi-target tracking, high-speed tracking, object detectors, vehicle tracking, real-time tracking
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