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In this paper we present an effective real-time video surveillance system for real-life outdoor surveillance scenarios. The system is an integration of two subsystems: the static camera and moving one. The approaches employed address properly the challenges that might arise in a typical outdoor scene, such as local and global lighting changes, variations in objects' appearance and occlusions. Our...
Most outdoor visual surveillance scenes involve objects of interest moving on the ground plane. However, perspective distortion introduces many difficulties to various applications like object classification and activity recognition. In this paper, we propose a robust automated method for both affine and metric rectification of the ground plane based on appearance and motion of vehicles in traffic...
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)...
Object classification and tracking are important in intelligent video surveillance systems. In this paper, an approach based on multiple overlapping cameras cooperation is proposed for object classification and tracking. In the proposed surveillance system, all the cameras are connected to the central computer server through network connection. Viewpoint correspondence and data fusion from multiple...
This study presents a novel intelligent sensor network for object detection, classification, and recognition. Wireless sensors are utilized as the first layer to identify the coordinates of unauthorized intruders. Cameras are employed to capture image features for object classification and recognition. To reduce processing time, a novel hierarchical image extraction scheme is developed. Global object...
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