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Real-Time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification
Open accessAbstract
Online multi-object tracking is a fundamental problem in time-critical video analysis applications. A major challenge in the popular tracking-by-detection framework is how to associate unreliable detection results with existing tracks. In this paper, we propose to handle unreliable detection by collecting candidates from outputs of both detection and tracking. The intuition behind generating redundant candidates is that detection and tracks can complement each other in different scenarios. Detection results of high confidence prevent tracking drifts in the long term, and predictions of tracks can handle noisy detection caused by occlusion. In order to apply optimal selection from a considerable amount of candidates in real-time, we present a novel scoring function based on a fully convolutional neural network, that shares most computations on the entire image. Moreover, we adopt a deeply learned appearance representation, which is trained on large-scale person re-identification datasets, to improve the identification ability of our tracker. Extensive experiments show that our tracker achieves real-time and state-of-the-art performance on a widely used people tracking benchmark.
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“Our tracking-by-attention approach achieves top performance via global attention without relying on additional motion [4, 10] or appearance models [4, 8, 10].”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [8]
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Self-citationMultiple object tracking with segmentation and interactive multiple model
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Min, Z., Hassan, G. M., & Jo, G. (2024) · IEEE Access · doi:10.1109/access.2024.3432156 · Cited by 5
Razak, R., & Abdullah, H. N. (2024) · Open Engineering · doi:10.1515/eng-2024-0056 · Cited by 8
Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
Cheng, X., Zhao, H., Deng, Y. X., & Shen, S. (2025) · Applied Sciences · doi:10.3390/app15020736 · Cited by 5
Multi-object Tracking with a Hierarchical Single-Branch Network
Wang, F., Luo, L., Zhu, E., & Wang, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-98355-0_7 · Cited by 4
Akabane, R., & Kato, Y. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378477 · Cited by 5
Wu, C., Yan, B., Yu, R., Yu, B., Zhou, X., Yu, Y.-L., & Na, C. (2021) · Personal and Ubiquitous Computing · doi:10.1007/s00779-021-01615-3 · Cited by 5
RetractedLightweight and Deep Appearance Embedding for Multiple Object Tracking
Ye, L., Li, W., Zheng, L., & Yuanyue, Z. (2022) · IET Computer Vision · doi:10.1049/cvi2.12106 · Cited by 5
Dynamic Gallery for Real-Time Multi-Target Multi-Camera Tracking
Chou, Y.-S., Wang, C.-Y., Chen, M., Lin, S.-D., & Liao, H.-Y. M. (2019) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2019.8909837 · Cited by 5
Metadata
- Authors
- Chen, L., Ai, H., Zhuang, Z., & Shang, C.
- Affiliations
- Tsinghua University (CN)
- Venue
- 2018 IEEE International Conference on Multimedia and Expo (ICME)
- Publisher
- IEEE
- Published
- 2018-07
- Pages
- 1-6
- OpenAlex
- W2889935068
- Cited by
- 530
- References
- 21
- Citations by year
- 2026: 25 · 2025: 53 · 2024: 97 · 2023: 75 · 2022: 88 · 2021: 72 · 2020: 72 · 2019: 41 · 2018: 7
- FWCI
- 14.46
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Multimedia and Expo (ICME) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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
- multi-target tracking, human tracking, real-time tracking, candidate selection, person re-identification, tracking-by-detection, occlusion handling, fully convolutional network, appearance representation, tracking drift
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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.
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