Identifying moving objects from a video sequence is a fundamental and critical task in many computer vision applications and a robust segmentation of motion objects from the static background is generally required. Segmented foreground objects generally include their self shadows as foreground objects since the shadow intensity differs and gradually changes from the background in a video sequence. Moreover, self shadows are vague in nature and have no clear boundaries. To eliminate such shadows from motion segmented video sequences, we propose an algorithm based on inferential statistical one way ANalysis Of VAriance (ANOVA) F test. This statistical model can deal scenes with complex and time varying illuminations without restrictions on the number of light sources and surface orientations. Results obtained with different indoor and outdoor sequences show that algorithm can effectively and robustly detect associated self shadows from segmented frames.
We propose an approach to track moving objects (humans) using optical flow in surveillance videos in this paper. We combine object segmentation output with optical flow algorithm while tracking object. That is, the proposed algorithm uses the object segmentation results while calculating optical flow and optical flow is only calculated in silhouette regions of motion using Two Way ANOVA. We track silhouettes (possible human torso), since these are more robust to variations in lighting conditions. The experimental results have demonstrated that our approach achieved good performance and the operating speed is relatively lower than some of the other standard optical flow techniques. We test our approach on several video surveillance sequences, both in indoor and outdoor.
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