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Across-camera object tracking using a conditional random field model

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Abstract

To ensure safety, most public spaces now deploy monitoring systems. However, in most scenarios, the tracking operations of these monitoring systems are performed manually. These operations should be automated. This paper proposes using a conditional random field (CRF) to formulate the automatic execution problem as a cost minimization problem. The appearance of pedestrians and the time taken by them to cross the view of a camera are used to solve the automatic execution problem. Crowd psychology is used to define constraints and construct a CRF graph. A Siamese convolutional neural network is employed to recognize pedestrian appearance. The time spent by pedestrians crossing the view of a camera is modeled using a normal distribution. The results of two models are considered as the costs of nodes and edges. The proposed algorithm is applied under constraints to determine matches at the minimum cost. The accuracy of the proposed method is compared with that of other methods by using common datasets and benchmarks. Superior results are obtained when both appearance and spatiotemporal information are employed for solving the automatic execution problem than when using appearance alone.

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Acknowledgements

This work was supported in part by the "Allied Advanced Intelligent Biomedical Research Center, STUST" from Higher Education Sprout Project, Ministry of Education, Taiwan, and in part by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-218-026.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, 701, Taiwan

    Sheng-Tzong Cheng, Chih-Wei Hsu & Sz-Yu Chen

  2. Department of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Tainan, Taiwan

    Gwo-Jiun Horng

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  1. Sheng-Tzong Cheng
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  2. Chih-Wei Hsu
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  3. Gwo-Jiun Horng
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  4. Sz-Yu Chen
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Correspondence to Gwo-Jiun Horng.

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Cheng, ST., Hsu, CW., Horng, GJ. et al. Across-camera object tracking using a conditional random field model. J Supercomput 77, 14252–14279 (2021). https://doi.org/10.1007/s11227-021-03862-w

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  • DOI: https://doi.org/10.1007/s11227-021-03862-w

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