W4: real-time surveillance of people and theiractivities

W⁴ is a real time visual surveillance system for detecting and tracking multiple people and monitoring their activities in an outdoor environment. It operates on monocular gray-scale video imagery, or on video imagery from an infrared camera. W⁴ employs a combination of shape analysis and tracking to locate people and their parts (head, hands, feet, torso) and to create models of people's appearance so that they can be tracked through interactions such as occlusions. It can determine whether a foreground region contains multiple people and can segment the region into its constituent people and track them. W⁴ can also determine whether people are carrying objects, and can segment objects from their silhouettes, and construct appearance models for them so they can be identified in subsequent frames. W⁴ can recognize events between people and objects, such as depositing an object, exchanging bags, or removing an object. It runs at 25 Hz for 320×240 resolution images on a 400 MHz dual-Pentium II PC     

Backpack: Detection of People Carrying Objects Using Silhouettes

We describe a video-rate surveillance algorithm for determining whether people are carrying objects or moving unencumbered from a stationary camera. The contribution of the paper is the shape analysis algorithm that both determines whether a person is carrying an object and segments the object from the person so that it can be tracked, e.g., during an exchange of objects between two people. As the object is segmented, an appearance model of the object is constructed. The method combines periodic motion estimation with static symmetry analysis of the silhouettes of a person in each frame of the sequence. Experimental results demonstrate robustness and real-time performance of the proposed algorithm.     

Real-time multiple vehicle detection and tracking from a moving vehicle

Abstract. A real-time vision system has been developed that analyzes color videos taken from a forward-looking video camera in a car driving on a highway. The system uses a combination of color, edge, and motion information to recognize and track the road boundaries, lane markings and other vehicles on the road. Cars are recognized by matching templates that are cropped from the input data online and by detecting highway scene features and evaluating how they relate to each other. Cars are also detected by temporal differencing and by tracking motion parameters that are typical for cars. The system recognizes and tracks road boundaries and lane markings using a recursive least-squares filter. Experimental results demonstrate robust, real-time car detection and tracking over thousands of image frames. The data includes video taken under difficult visibility conditions. Received: 1 September 1998 / Accepted: 22 February 2000