Enhanced Multiple-Object Tracking Using Delay Processing and Binary-Channel Verification

Autor: Arjan Kuijper, Mu Zhiya, Xin He, Muyu Li, Jun Wang, Zhonghui Wei
Přispěvatelé: Publica
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Channel (digital image)
single object tracking
Computer science
02 engineering and technology
lcsh:Technology
lcsh:Chemistry
Consistency (database systems)
3D tracking
Robustness (computer science)
0202 electrical engineering
electronic engineering
information engineering
General Materials Science
Computer vision
identity consistency
Representation (mathematics)
Instrumentation
multiple object tracking
lcsh:QH301-705.5
object tracking
Fluid Flow and Transfer Processes
Research Line: Computer vision (CV)
consistency
business.industry
lcsh:T
Process Chemistry and Technology
General Engineering
Construct (python library)
Object (computer science)
lcsh:QC1-999
Lead Topic: Smart City
Computer Science Applications
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
Video tracking
Identity (object-oriented programming)
020201 artificial intelligence & image processing
Artificial intelligence
business
lcsh:Engineering (General). Civil engineering (General)
lcsh:Physics
Zdroj: Applied Sciences, Vol 9, Iss 22, p 4771 (2019)
Applied Sciences
Volume 9
Issue 22
Popis: Tracking 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&mdash
attaching, re-initialization, and re-claiming&mdash
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.
Databáze: OpenAIRE
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