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Multiple Reliable Structured Patches for Object Tracking
- Source :
- Cognitive Computation. 13:1593-1602
- Publication Year :
- 2020
- Publisher :
- Springer Science and Business Media LLC, 2020.
-
Abstract
- It is essential to build the effective appearance model for object tracking in computer vision. Most object trackers can be roughly divided into two categories according to the appearance model: the bounding box model and the patch model. The bounding box model cannot handle shape deformation and occlusion of the non-rigid moving object effectively. The patch model is prone to be disturbed by complex backgrounds. In this paper, we propose a robust multi-structured-patch appearance model to represent the target for object tracking. The proposed appearance model is aimed to exploit and identify reliable patches that can be tracked effectively through the whole tracking process. According to attention mechanism in biological vision system, a coarse-to-fine strategy is usually used to search the target. Therefore, the proposed appearance model is represented by robust patches in different sizes, in which the bigger patches search the rough region of the target and the smaller patches estimate the accurate location. Experimental results on OTB100 dataset show that the proposed method outperforms state-of-the-art trackers.
- Subjects :
- Machine vision
Computer science
business.industry
BitTorrent tracker
Cognitive Neuroscience
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Process (computing)
02 engineering and technology
Tracking (particle physics)
Object (computer science)
Computer Science Applications
Active appearance model
03 medical and health sciences
0302 clinical medicine
Minimum bounding box
Video tracking
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Computer vision
Computer Vision and Pattern Recognition
Artificial intelligence
business
030217 neurology & neurosurgery
ComputingMethodologies_COMPUTERGRAPHICS
Subjects
Details
- ISSN :
- 18669964 and 18669956
- Volume :
- 13
- Database :
- OpenAIRE
- Journal :
- Cognitive Computation
- Accession number :
- edsair.doi...........5c00e303e6d1ebea77ee04a58252a600