Answers are grounded in this paper and cite their sources.
Multiobject tracking is a basic task in video analysis. Due to the strict requirements on efficiency and resource consumption, most of the applications on edge devices are online or near-online methods. Besides motion modeling, appearance information is also widely used for tracking. However, the influence of occlusion is usually ignored. In this article, spatial-temporal co-occurrence constraints (STCCs) features are introduced to resist occlusions by exploring the rich spatial and temporal information of tracklets. In addition, a novel blockchain-based near-online framework called co-occurrence constraints tracklet tracker (CoCTs) is proposed for cross-camera tracking. It inherits the advantages of the blockchain technology in sharing information. Based on blockchain, an efficient association mechanism and a reliable information sharing method are introduced. Experimental results show that CoCT performs high computational efficiency and low resource consumption. In the edge computing environment, it achieves real-time performance on cross-camera tracking. On the MOT17 benchmark, our method shows the state-of-the-art results compared with other online trackers.
No takes yet. Share an insight, caveat, or question.
Sheng et al. (2020) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: