Osman Topcu

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Moving object segmentation with a nonstationary camera is a difficult problem due to the motion of both camera and the object. A moving object segmentation method is proposed in this work to be used in pan-tilt-zoom (PTZ) cameras. The method is based on composing scene mosaic and applying Gaussian mixture background subtraction algorithm after constructing(More)
Object tracking is an important element of computer vision algorithms. This problem is difficult due to occlusion, illumination changes and shadows. We propose an appearance based occlusion-aware method for object tracking. Proposed method is based on particle filter tracking in a multi-camera environment. In this method, observations involving both(More)
Targets with low signal to noise ratio are difficult to detect. Track-before-detect algorithm detects weak targets by sufficiently tracking them. Particle filter track-before-detect algorithm is flexible enough to be modified for occlusion handling. The first novelty of this work, is the modification of the particle filter track-before-detect algorithm to(More)
Multiple object tracking within a network of cameras with overlapping fields of views has gained interest. The acquisition of images in an asynchronous manner hinders the practical implementation of such systems. Most of the previous work reported tests over short intervals, leaving the performance degradation due to asynchronous image acquisition unknown.(More)
Visual tracking has an important place among computer vision applications. Visual tracking with particle filters is a well-known methodology. The performance of particle filters is dependent on efficient sampling of the state space, which in turn, is dependent on number of particles. In this paper, Rao-Blackwell technique is applied to particle filters to(More)
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