M.D. Jain and S.N. Pradeep (India)
Object Segmentation, tracker, five features, centroid, shape, color histogram
The proposed video surveillance method comprises segmentation of moving targets and tracking the detected objects through five features of the target. We introduce motion object segmentation by fusion of 3-frame temporal differencing and edge-based detection, which is further updated by a median filter. The combination of the five features spatial positions, LBW, Compactness, Orientation and color histogram through particle filter approach tracks the segmented objects. These five features help in matching the target tracks during occlusions, merging of targets, stop and go motion in vary challenging environmental (rainy and snowy) conditions shown in the results. Our proposed method provides solution to common problems related to matching of target tracks. We provide encouraging experimental results calculated on synthetic and real world sequences to demonstrate the algorithm performance.
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