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1、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 Comment M1: two orders of magnitude，表示“两个数量级” ，科技文中常用的数量级比较， “larger than”也表明比较级， “any other”常用于比较级中译为“其他任何” 。Comment M2: “such as 比如、诸如” 相当于 like或 f
2、or example。Comment M3: 英语中多个短语并列时，在最后一个处用 and 连接，其余的用逗号连接，注意英语中无顿号的用法，这点注意与汉语的差别。Pedestrian Detection and Tracking Using Deformable Part Models and Kalman FilteringXue Fan, Shubham Mittal, Twisha Prasad, Suraj Saurabh and Hyunchul ShinReceived: February 23, 2013 /Accepted: March 22, 2013 /Published：
3、July 31, 2013Abstract: 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 mul
4、ti-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. We test and demonstrate
5、 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
6、be tracked correctly after a group of people split. Experiments 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 Kal
7、man filtering.Key words：multi-person tracking, deformable part models, data association, Kalman Filtering, pedestrian detection.1. IntroductionPedestrian detection and tracking are important in a wide range of applications in our daily lives, such as surveillance systems, airport security, automatic
8、 driving, and driver assistance systems in high-end cars, human-robot interaction, and assistance for senior citizens. Detecting pedestrians in images is a challenging task owing to various styles of clothing in appearance and huge possible postures. Significant research has been devoted to detectin
9、g, locating, and tracking people in images and videos. The goal of this paper is to enable reliable multi-person tracking from a moving platform in real-world scenarios. In this paper, the authors present an integrated tracking-by-detection framework which is able to detect and track multiple person
10、s even in challenging scenarios such as instances where the objects are partially occluded for extended periods of time. We integrated formable part based models Comment M4: Which引导主语从句Comment M5: result from 也是常用短语，译为“起因于；由造成”with Kalman filtering. We use the detector of Ref. 1 and modify the sourc
11、e code to train it on Caltech Pedestrian Dataset2The main challenge when using the pedestrian detector for tracking is that the detector output is unreliable. The detection output sometimes consists of some false positives and missed detections. Thus, the resulting association problem between detect
12、ions and targets is difficultSeveral recent algorithms address this problem by optimizing detection assignments over a large temporal window in an offline step3,4With the improvement of detection algorithms 1，both in accuracy and computational feasibility，tracking-by-detection is one of the most pop
13、ular concepts for tracking5 Due to the high amount of false positives and missing detections in the output of the detector，it is necessary to incorporate temporal contextRecent tracking approaches6 try to associate the detections and track objects from uncalibrated single cameraRecently，pedestrian d
14、etection methods have achieved great improvements7,8，which have allowed the scientific community to focus on associating tracklets which are initially linked by detection responses in consecutive frames Next，short tracklets are associated to form longer ones by maximizing the probabilities globally
15、using the similarity and discrimination scores both from appearance and motion modelsBy using the information of the previous，current，and future frames，these methods rely on the spatio-temporal restriction to improve the results with different types of features from simple object signatures such as height，width to more complicated ones like local gradient intensity featuresThe most important factor to improve the tracking results is building the affinity scores between two tra
