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Moving object classification in far-field video is a key component of smart surveillance systems. In this paper, we propose a reliable system for person-vehicle classification which works well in challenging real-word conditions, including the presence of shadows, low resolution imagery, perspective distortions, arbitrary camera viewpoints, and groups of people. Our system runsin real-time (30 Hz)...
In this paper, we present retrieval methods for extracting colored moving objects from surveillance cameras. In particular, we describe a method to classify moving objects into one of six colors. The method includes two sets of parameters. The first set can be used to compensate for illumination conditions and camera differences. The second set is used to tune the color extraction for specific object...
Detecting foreground object often need to face the problems of illumination change and image noise. In this paper, we propose an object detection method using two successive image frames. Illumination change would be very small in such short time, and then we can handle the first problem more easily. Image noise will confuse the detection of an object boundary. To handle this problem, we apply level...
In this paper we describe a multi-camera traffic monitoring system relying on the concept of probability fusion maps (PFM) to detect vehicles in a traffic scene. In the PFM, traffic images from multiple cameras are inverse perspective-mapped and registered onto a common reference frame, combining the multiple camera information to reduce the impact of occlusions. Although the unconstrained perspective...
This paper describes a learning-based approach to recognizing shapes in video sequences using spatial and temporal features of the shape. The spatial characteristics are encoded in the mean frame, while the temporal characteristics are extracted using the Iwasawa decomposition of the shape sequence. Training is done using logistic regression, namely the LogitBoost algorithm. The method obtains good...
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