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HPMC: A Multi-target Tracking Algorithm for the IoT

Xinyue Lv1, Xiaofeng Lian2,*, Li Tan1, Yanyan Song1, Chenyu Wang3

1 School of Computer Science and Engineering, Beijing Technology and Business University, Beijing, 100048, China
2School of Artificial Intelligence, Beijing Technology and Business University, Beijing, 100048, China
3 School of Science and Engineering, University of Dundee, Dundee, DD1 4HN, UK

* Corresponding Author: Xiaofeng Lian. Email: email

Intelligent Automation & Soft Computing 2021, 28(2), 513-526.


With the rapid development of the Internet of Things and advanced sensors, vision-based monitoring and forecasting applications have been widely used. In the context of the Internet of Things, visual devices can be regarded as network perception nodes that perform complex tasks, such as real-time monitoring of road traffic flow, target detection, and multi-target tracking. We propose the High-Performance detection and Multi-Correlation measurement algorithm (HPMC) to address the problem of target occlusion and perform trajectory correlation matching for multi-target tracking. The algorithm consists of three modules: 1) For the detection module, we proposed the You Only Look Once(YOLO)v3_plus model, which is an improvement of the YOLOv3 model. It has a multi-scale detection layer and a repulsion loss function. 2) The feature extraction module extracts appearance, movement, and shape features. A wide residual network model is established, and the coefficient k is added to extract the appearance features of the target. 3) In the multi-target tracking module, multi-correlation measures are used to fuse the three extracted features to increase the matching degree of the target track and improve the tracking performance. The experimental results show that the proposed method has better performance for small and occluded targets than comparable algorithms.


Cite This Article

X. Lv, X. Lian, L. Tan, Y. Song and C. Wang, "Hpmc: a multi-target tracking algorithm for the iot," Intelligent Automation & Soft Computing, vol. 28, no.2, pp. 513–526, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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