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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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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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