Tracking and Object Classification for Automated Surveillance

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 ...

• Type: Conference
• Year: 2002
• Where: ECCV
• Authors: Omar Javed, Mubarak Shah
• Keywords: Computer Vision, ECCV 2002, Outdoor Surveillance Systems, PETS 2001 Sequences, Real World Sequences, Recurrent Motion Image, Repeated Motion