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Tracking objects using Mean Shift algorithm fails when there is a full/partial occlusion or when the background color and the desired object are close. In this paper we proposed a method using Kalman Filter and Mean Shift for handling these situations. Using similarity measure of Mean Shift algorithm we are able to detect an occlusion. Kalman Filter comes into the play for occlusion handling in a Buffer-Mode Process. We implemented this algorithm both on PC and DSP 64x+ Texas Instrument and the results are both tabulated. The results reveal the ability of our method to locate the object soon after occlusion disappearance.

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Automatic analysis and understanding of typical activities and identification of abnormal events in crowded traffic scenes is a fundamental task for traffic video surveillance. In this paper, we address the problem of abnormality detection based on an unsupervised learning approach with Fully Sparse Topic Models (FSTM). The method uses a set of visual features and automatically discovers the activity patterns occurring in complicated scenes. We show how the discovered patterns can be used to detect abnormal events. Furthermore, we compare FSTM with other topic models based on various measures. Experimental results and comparisons on two traffic datasets demonstrate that our approach outperforms other methods in finding meaningful activity patterns and discovers the abnormal events accurately.

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