Pedestrian tracking framework based on deep convolution network and SIFT
Jing Liu, Kai Chen, Xiao SONG · Scientia Sinica Informationis · 2018
Numerous object-tracking and multiple people-tracking algorithms have been put forward in the field of computer vision, but these algorithms did not address the issue of when a pedestrian is partially or fully occluded by another object or person. To achieve efficient pedestrian tracking in various occlusion conditions, this research presents a pedestrian-tracking framework based on deep learning. First, a pedestrian detector was trained as a pedestrian-tracking search mechanism based on the object detection algorithm Faster R-CNN, which narrows the search range and improves accuracy efficiently compared with traditional gradient-down algorithms. Second, color histogram and scale-invariant features transform (SIFT) were combined as the target model expression. In the process of target matching, a full convolution network (FCN) was trained for pedestrians to extract the pedestrian information in the target model base on an FCN image semantic segmentation algorithm for removing the background noise. Finally, extensive experiments on OTB demonstrate that the proposed method achieves better performance than other state-of-the-art trackers for precision and success rate.