The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Track-Clustering Error Evaluation for Track-Based Multi-camera Tracking System Employing Human Re-identification
Abstract
In this study, we present a set of new evaluation measures for the track-based multi-camera tracking (T-MCT) task leveraging the clustering measurements. We demonstrate that the proposed evaluation measures provide notable advantages over previous ones. Moreover, a distributed and online T-MCT framework is proposed, where re-identification (Re-id) is embedded in T-MCT, to confirm the validity of the proposed evaluation measures. Experimental results reveal that with the proposed evaluation measures, the performance of T-MCT can be accurately measured, which is highly correlated to the performance of Re-id. Furthermore, it is also noted that our T-MCT framework achieves competitive score on the DukeMTMC dataset when compared to the previous work that used global optimization algorithms. Both the evaluation measures and the inter-camera tracking framework are proven to be the stepping stone for multi-camera tracking.
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Performance in single-camera MOT depends on the quality of detection and occlusion.
“Performance in this type of tracking depends on the quality of detection and occlusion [28] as a single camera provides a one-sided view.”
From Real-Time Vehicle Classification and Tracking Using a Transfer Learning-Improved Deep Learning Network (2022) · cites this paper as [28]
Citations
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HOTA: A Higher Order Metric for Evaluating Multi-object Tracking
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HOTA: A Higher Order Metric for Evaluating Multi-object Tracking
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“Performance in this type of tracking depends on the quality of detection and occlusion [28] as a single camera provides a one-sided view.”
BackgroundMultiple hypothesis tracking algorithm for multi‐target multi‐camera tracking with disjoint views
Yoon, K., Song, Y.-M., & Jeon, M. (2018) · IET Image Processing · doi:10.1049/iet-ipr.2017.1244 · Cited by 71
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“It enables the enhancement of trackers to track multiple objects in a video frame sequence based on the detection quality [27].”
BackgroundMethodMultiple Object Tracking in Deep Learning Approaches: A Survey
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State-Aware Re-Identification Feature for Multi-Target Multi-Camera Tracking
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Rethinking Multi-Object Tracking Based on Re-Identification and Appearance Model Management
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Towards Automated Spatio-Temporal Trajectory Recovery in Wide-Area Camera Networks
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Multiplayer Tracking with Diagonal Video to Support Basketball Tactical Learning
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Improvement of Tracking Accuracy Using Travel Pattern for Agent-Based Human Tracking
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Reduction of Agents Based on Appearance Probability for Agent-based Human Tracking System
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Multi-camera Tracking Based on Spatio-Temporal Association in Small Overlapping Regions
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Limitation of Agents in a Node for Agent-Based Human Tracking System
Tanigawa, K., Shiozuka, M., & Takahashi, K. (2024) · IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) · doi:10.1109/iiai-aai-winter65925.2024.00052
Metadata
- Authors
- Wu, C.-W., Zhong, M.-T., Tsao, Y., Yang, S.-W., Chen, Y.-K., & Chien, S.-Y.
- Affiliations
- National Taiwan University (TW)
- Academia Sinica (TW)
- Intel (United States) (US)
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 1416-1424
- OpenAlex
- W2740393196
- Cited by
- 18
- References
- 21
- Citations by year
- 2024: 5 · 2023: 1 · 2022: 3 · 2021: 3 · 2020: 3 · 2019: 1 · 2018: 2
- FWCI
- 0.48
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- Funders
- Ministry of Science and Technology, Taiwan
- 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 › Information Systems › Data Management and Algorithms
- Keywords
- track clustering, evaluation metrics, person re-identification
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