Tracking and object classification for automated surveillance
Computer Science, Artificial Intelligence; Computer Science, Theory &; Methods; Imaging Science & Photographic Technology
In this paper we discuss the issues that need to be resolved before fully automated outdoor surveillance systems can be developed, and present solutions to some of these problems. Any outdoor surveillance system must be able to track objects moving in its field of view, classify these objects and detect some of their activities. We have developed a method to track and classify these objects in realistic scenarios. Object tracking in a single camera is performed using background subtraction, followed by region correspondence. This takes into account multiple cues including velocities, sizes and distances of bounding boxes. Objects can be classified based on the type of their motion. This property may be used to label objects as a single person, vehicle or group of persons. Our proposed method to classify objects is based upon detecting recurrent motion for each tracked object. We develop a specific feature vector called a 'Recurrent Motion Image' (RMI) to calculate repeated motion of objects. Different types of objects yield very different RMI's and therefore can easily be classified into different categories on the basis of their RMI. The proposed approach is very efficient both in terms of computational and space criteria. RMI's are further used to detect carried objects. We present results on a large number of real world sequences including the PETS 2001 sequences. Our surveillance system works in real time at approximately 15Hz for 320x240 resolution color images on a 1.7 GHz pentium-4 PC.
Computer Vision - Eccv 2002, Pt Iv
"Tracking and object classification for automated surveillance" (2002). Faculty Bibliography 2000s. 3269.