Abstract
One of the core challenges in visual multi-target tracking is occlusion. This is especially important in applications such as video surveillance and sports analytics. While offline batch processing algorithms can utilise future measurements to handle occlusion effectively, online algorithms have to rely on current and past measurements only. As such, it is markedly more challenging to handle occlusion in online applications. To address this problem, we propagate information over time in a way that it generates a sense of déjà vu when similar visual and motion features are observed. To achieve this, we extend the Generalized Labeled Multi-Bernoulli (GLMB) filter, originally designed for tracking point-sized targets, to be used in visual multi-target tracking. The proposed algorithm includes a novel false alarm detection/removal and label recovery methods capable of reliably recovering tracks that are even lost for a substantial period of time. We compare the performance of the proposed method with the state-of-the-art methods in challenging datasets using standard visual tracking metrics. Our comparisons show that the proposed method performs favourably compared to the state-of-the-art methods, particularly in terms of ID switches and fragmentation metrics which signifies occlusion.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Cited by
16 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Integration of Intelligent Driver Model with Interaction-Aware LMB (IA-LMB) Filter for Vehicle tracking;2023 12th International Conference on Control, Automation and Information Sciences (ICCAIS);2023-11-27
2. A New Approach of Midrange Exploration Exploitation Searching Particle Swarm Optimization for Optimal Solution;2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS);2023-08-25
3. Modeling Inter-Vehicle Occlusion Scenarios in Multi-Camera Traffic Surveillance Systems;2023 26th International Conference on Information Fusion (FUSION);2023-06-28
4. Feature-Based Object Detection and Tracking: A Systematic Literature Review;International Journal of Image and Graphics;2023-02-03
5. Distributed Complementary Fusion for Connected Vehicles;2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS);2022-11-21