CIOMP OpenIR
Enhanced Multiple-Object Tracking Using Delay Processing and Binary-Channel Verification
M.Y.Li; X.He; Z.H.Wei; J.Wang; Z.Y.Mu; A.Kuijper
2019
Source PublicationApplied Sciences-Basel
Volume9Issue:22Pages:19
AbstractTracking objects over time, i.e., identity (ID) consistency, is important when dealing with multiple object tracking (MOT). Especially in complex scenes with occlusion and interaction of objects this is challenging. Significant improvements in single object tracking (SOT) methods have inspired the introduction of SOT to MOT to improve the robustness, that is, maintaining object identities as long as possible, as well as helping alleviate the limitations from imperfect detections. SOT methods are constantly generalized to capture appearance changes of the object, and designed to efficiently distinguish the object from the background. Hence, simply extending SOT to a MOT scenario, which consists of a complex scene with spatially mixed, occluded, and similar objects, will encounter problems in computational efficiency and drifted results. To address this issue, we propose a binary-channel verification model that deeply excavates the potential of SOT in refining the representation while maintaining the identities of the object. In particular, we construct an integrated model that jointly processes the previous information of existing objects and new incoming detections, by using a unified correlation filter through the whole process to maintain consistency. A delay processing strategy consisting of the three parts-attaching, re-initialization, and re-claiming-is proposed to tackle drifted results caused by occlusion. Avoiding the fuzzy appearance features of complex scenes in MOT, this strategy can improve the ability to distinguish specific objects from each other without contaminating the fragile training space of a single object tracker, which is the main cause of the drift results. We demonstrate the effectiveness of our proposed approach on the MOT17 challenge benchmarks. Our approach shows better overall ID consistency performance in comparison with previous works.
Keywordmultiple object tracking,identity consistency,single object tracking,association,Chemistry,Engineering,Materials Science,Physics
DOI10.3390/app9224771
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Indexed BySCI
Language英语
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Document Type期刊论文
Identifierhttp://ir.ciomp.ac.cn/handle/181722/63262
Collection中国科学院长春光学精密机械与物理研究所
Recommended Citation
GB/T 7714
M.Y.Li,X.He,Z.H.Wei,et al. Enhanced Multiple-Object Tracking Using Delay Processing and Binary-Channel Verification[J]. Applied Sciences-Basel,2019,9(22):19.
APA M.Y.Li,X.He,Z.H.Wei,J.Wang,Z.Y.Mu,&A.Kuijper.(2019).Enhanced Multiple-Object Tracking Using Delay Processing and Binary-Channel Verification.Applied Sciences-Basel,9(22),19.
MLA M.Y.Li,et al."Enhanced Multiple-Object Tracking Using Delay Processing and Binary-Channel Verification".Applied Sciences-Basel 9.22(2019):19.
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