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- Title
Real-Time Tracking Framework with Adaptive Features and Constrained Labels.
- Authors
Daqun Li; Tingfa Xu; Shuoyang Chen; Jizhou Zhang; Shenwang Jiang
- Abstract
This paper proposes a novel tracking framework with adaptive features and constrained labels (AFCL) to handle illumination variation, occlusion and appearance changes caused by the variation of positions. The novel ensemble classifier, including the Forward–Backward error and the location constraint is applied, to get the precise coordinates of the promising bounding boxes. The Forward–Backward error can enhance the adaptation and accuracy of the binary features, whereas the location constraint can overcome the label noise to a certain degree. We use the combiner which can evaluate the online templates and the outputs of the classifier to accommodate the complex situation. Evaluation of the widely used tracking benchmark shows that the proposed framework can significantly improve the tracking accuracy, and thus reduce the processing time. The proposed framework has been tested and implemented on the embedded system using TMS320C6416 and Cyclone III kernel processors. The outputs show that achievable and satisfying results can be obtained.
- Subjects
TRACKING &; trailing; BOXES; CONTAINERS; GRADING (Commercial products); CONSUMER protection
- Publication
Sensors (14248220), 2016, Vol 16, Issue 9, p1449
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s16091449