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We propose an extension of a neural-based background subtraction approach to moving object detection to the case of image sequences taken from pan-tilt-zoom (PTZ) cameras. The background model automatically adapts in a self-organizing way to changes in the scene background. Background variations arising in a usual stationary camera setting, such as those due to gradual illumination changes, to waving...
This paper presents a novel disparity map refinement method and vision based surveillance framework for the task of detecting objects of interest in dynamic outdoor environments from two stereo video sequences taken at different times and from different viewing angles by a mobile camera platform. The proposed framework includes several steps, the first of which computes disparity maps of the same...
Over the last one decade there has been an increasing emphasis on driver-assistance systems for the automotive domain. In this paper we report our work on designing a camera-based surveillance system embedded in a ??smart?? car door. Such a camera is used to monitor the ambient environment outside the car - e.g., the presence of obstacles such as approaching cars or cyclists who might collide with...
The problem of ground truth generation is fundamental for many approaches of computer vision and image processing. In order to test algorithms for object segmentation, object tracking, object interactions, it is necessary to have image sequences in which the ground truth is determined in an objective way. In the context of visual surveillance where many people moves in the scene occluding each other,...
This paper provides a comprehensive quantitative comparison of metrics for detecting visual anomalies between two videos that are recorded along same path but at different times by a camera on a patrolling platform. The metrics used in this paper are histogram based metrics, statistic based metrics and pixel differences based metrics. We test the metrics for the detection of mobile and stationary...
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