An Integrated Deep Learning Framework for Occluded Pedestrian Tracking
| Autor: | Baochang Zhang, Kai Chen, Baocun Hou, Wang Yi, Xiao Song, Xiang Zhai |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Color histogram
General Computer Science Computer science Faster R-CNN Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 010501 environmental sciences 01 natural sciences Histogram SIFT 0202 electrical engineering electronic engineering information engineering General Materials Science Computer vision Segmentation 0105 earth and related environmental sciences business.industry Deep learning color histogram General Engineering Pedestrian tracking FCN Kernel (image processing) Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering Gradient descent business lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 7, Pp 26060-26072 (2019) |
| ISSN: | 2169-3536 |
| Popis: | Numerous object-tracking and multiple-person-tracking algorithms have been developed in the field of computer vision, but few trackers can properly address the issue of when a pedestrian is partially or fully occluded by other objects or persons. In order to achieve efficient pedestrian tracking in various occlusion conditions, a pedestrian tracking framework is proposed and developed based on the deep learning networks. First, a pedestrian detector is trained as a tracking mechanism based on the Faster R-CNN, which narrows the search range and efficiently improves accuracy, as compared with the traditional gradient descent algorithm. Second, in the process of target matching, a color histogram and scale-invariant feature transform are combined to provide the target model expression, and a full convolution network (FCN) is trained to extract the pedestrian information in the target model, based on an FCN image semantic segmentation algorithm that can remove background noise effectively. Finally, the extensive experiments on a commonly used tracking benchmark show that the proposed method achieves better performance than the other state-of-the-art trackers in various occlusion situations. |
| Databáze: | OpenAIRE |
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