Detection and tracking of moving objects in a video has been emerging as a demanding research in the domain of computer vision and image processing in the resent years. It has been used in various applications like visual surveillance, traffic monitoring etc for tracking interested objects. An efficient method for object detection and tracking is proposed in this work. Two discriminative visual features like spatial edges and temporal motion boundaries as indicators for foreground object locations are considered. Initially frame wise spatiotemporal saliency maps by making use of geodesic distance indicators are created. Geodesic distance also provides an initial estimation for background and foreground by building on the observation that foreground areas are surrounded by the regions with high patio temporal edge values. Coherent object segmentation is done by combining all this spatio temporal maps. Finally the segmented object is tracked using Kalman filter get efficient result

Spatial edges, Temporal motion boundaries, Spatiotemporal saliency maps, Geodesic distance, Kalman filter, Visual surveillance, Pixel segmentation, super pixels.

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