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.
- K-wide residual network model
- Multi-correlation measurement
- Multi-target tracking
ASJC Scopus subject areas
- Theoretical Computer Science
- Computational Theory and Mathematics
- Artificial Intelligence