Video processing methods robust to illumination variations
Çetin, A. Enis
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Moving shadows constitute problems in various applications such as image segmentation, smoke detection and object tracking. Main cause of these problems is the misclassification of the shadow pixels as target pixels. Therefore, the use of an accurate and reliable shadow detection method is essential to realize intelligent video processing applications. In the first part of the thesis, a cepstrum based method for moving shadow detection is presented. The proposed method is tested on outdoor and indoor video sequences using well-known benchmark test sets. To show the improvements over previous approaches, quantitative metrics are introduced and comparisons based on these metrics are made. Most video processing applications require object tracking as it is the base operation for real-time implementations such as surveillance, monitoring and video compression. Therefore, accurate tracking of an object under varying scene and illumination conditions is crucial for robustness. It is well known that illumination variations on the observed scene and target are an obstacle against robust object tracking causing the tracker lose the target. In the second part of the thesis, a two dimensional (2D) cepstrum based approach is proposed to overcome this problem. Cepstral domain features extracted from the target region are introduced into the covariance tracking algorithm and it is experimentally observed that 2D-cepstrum analysis of the target region provides robustness to varying illumination conditions. Another contribution is the development of the co-difference matrix based object tracking instead of the recently introduced covariance matrix based method. One of the problems with most target tracking methods is that they do not have a well-established control mechanism for target loss which usually occur when illumination conditions suddenly change. In the final part of the thesis, a confidence interval based statistical method is developed for target loss detection. Upper and lower bound functions on the cumulative density function (cdf) of the target feature vector are estimated for a given confidence level. Whenever the estimated cdf of the detected region exceeds the bounds it means that the target is no longer tracked by the tracking algorithm. The method is applicable to most tracking algorithms using features of the target image region.
KeywordsMoving Shadow Detection
Object Tracking under Illumination Conditions
Target Loss Detection