Dong Yin

Zhipeng Zhou, Rui Zhang, Dong Yin

Object tracking has important application in assistive technologies for personalized monitoring. Recent trackers choosing AlexNet as their backbone to extract features have gained great success. However, AlexNet is too shallow to form a strong feature representation, the tracker based on the Siamese network have an accuracy gap compared with state-of-the-art algorithms. To solve this problem, this paper proposes a tracker called SiamPF. Firstly, the modified pre-trained VGG16 network is fine-tuned as the backbone. Secondly, an AlexNet-like branch is added after the third convolutional layer and merged with the response map of the backbone network to form a preliminary strong feature representation. And then, a channel attention block is designed to adaptively select the contribution features. Finally, the APCE is modified to process the response map to reduce interference and focus the tracker on the target. Our SiamPF only used ILSVRC2015-VID for training, but it achieved excellent performance on OTB-2013 / OTB-2015 / VOT2015 / VOT2017, while maintaining the real-time performance of 41FPS on the GTX 1080Ti.

Mingyue Yuan, Dong Yin, Jingwen Ding et al.

Person re-identification plays an important role in realistic video surveillance with increasing demand for public safety. In this paper, we propose a novel framework with rules of updating images for person re-identification in real-world surveillance system. First, Image Pool is generated by using mean-shift tracking method to automatically select video frame fragments of the target person. Second, features extracted from Image Pool by convolutional network work together to re-rank original ranking list of the main image and matching results will be generated. In addition, updating rules are designed for replacing images in Image Pool when a new image satiating with our updating critical formula in video system. These rules fall into two categories: if the new image is from the same camera as the previous updated image, it will replace one of assist images; otherwise, it will replace the main image directly. Experiments are conduced on Market-1501, iLIDS-VID and PRID-2011 and our ITSD datasets to validate that our framework outperforms on rank-1 accuracy and mAP for person re-identification. Furthermore, the update ability of our framework provides consistently remarkable accuracy rate in real-world surveillance system.