Object Tracking in Structured Environments for Video Surveillance Applications
Abstract
We present a novel tracking method for effectively tracking objects in structured environments. The tracking method finds applications in security surveillance, traffic monitoring, etc. In these applications, the movements of objects are constrained by structured environments. Therefore, the relationship between objects and environments can be exploited as additional information for improving the performance of tracking. We use the environment state to model the relationship between the objects and environments, and integrate it into the framework of Bayesian tracking. In this paper, distance transform is used to model the environment state, and particle filtering is employed as the paradigm for solving the Bayesian tracking problem. The adaptive dynamics model and environment prior are devised for the particle filter to fully utilize the environment information in the tracking process. Experiments on some video surveillance sequences demonstrate the effectiveness and robustness of our approach for tracking object motions in structured environments.
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In [92], a distance transform state and particle filtering were used to determine safe boundaries for objects such as a person walking on the road.
“Zhu et al. [92] achieved this by utilizing a distance transform state and particle filtering to determine safe boundaries for objects like a person walking on the road.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [92]
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- Authors
- Zhu, J., Lao, Y., & Zheng, Y. F.
- Affiliations
- The Ohio State University (US)
- Shanghai Jiao Tong University (CN)
- ORCID
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2010-02
- Volume
- 20
- Issue
- 2
- Pages
- 223-235
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W2127412017
- Cited by
- 93
- References
- 32
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3 · 2023: 5 · 2022: 3 · 2021: 3 · 2020: 3 · 2019: 3 · 2018: 4 · 2017: 4 · 2016: 9 · 2015: 6 · 2014: 7 · 2013: 16 · 2012: 11
- FWCI
- 8.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Circuits and Systems for Video Technology (publishedVersion)
- 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 › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Keywords
- object tracking, structured environments, video surveillance, Bayesian tracking, particle filter, distance transform, environment modeling, traffic monitoring, security monitoring
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