专业英语之文献语言点评论.doc

科技英语阅读与写作 题 目 Pedestrian Detection and Tracking Using Deformable Part Models and Kalman Filtering 学 院 专 业 姓 名 soppa=face like this 学 号 parterner 姓 名 学 号 Pedestrian Detection and Tracking Using Deformable Part Models and Kalman Filtering Xue Fan, Shubham Mittal, Twisha Prasad, Suraj Saurabh and Hyunchul Shin Received: February 23, 2013 /Accepted: March 22, 2013 /Published：July 31, 2013 Abstract: Pedestrian detection techniques are important and challenging especially for complex real world scenes. They can be used for ensuring pedestrian safety, ADASs(advance driver assistance systems) and safety surveillance systems. In this paper, we propose a novel approach for multi-person tracking-by-detection using deformable part models in Kalman filtering framework. The Kalman filter is used to keep track of each person and a unique label is assigned to each tracked individua1. Based on this approach, people can enter and leave the scene randomly. ①We test and demonstrate our results on Caltech Pedestrian benchmark. which is two orders of magnitude larger than any other existing datasets and consists of pedestrians varying widely in appearance, pose and scale. Complex situations such as people occluded by each other are handled gracefully and individual persons can be tracked correctly after a group of people split. ②Experiments confirm the real-time performance and robustness of our system, working in complex scenes. Our tracking model gives a tracking accuracy of 72.8% and a tracking precision of 82.3%. We can further reduce false positives by 2.8%, using Kalman filtering. Key words：multi-person tracking, deformable part models, data association, Kalman Filtering, pedestrian detection. Introduction ③Pedestrian dete