A. Ambardekar, Mircea Nicolescu, and Monica Nicolescu (USA)
Computer vision, object tracking, and clustering analysis
Object tracking is a complex, yet essential task to be addressed in any video surveillance application. Many real-time techniques proposed in the literature rely on a frame-to-frame matching of objects. This paper describes a technique which takes into consideration the inherent temporal coherence that exists across frames, thus being able to robustly perform tracking while handling difficult situations such as object acceleration and partial occlusion. SIFT (Scale Invariant Feature Transform) approaches have been shown to perform well for object recognition, due to their robustness to noise, changes in illumination and viewpoint. In this work we propose to use a SIFT-based method for tracking image features across frames. Tracked SIFT features provide the displacement of each interest point in the image, which along with image coordinates and frame number constitute a feature vector. All feature vectors are added to a temporal buffer and clustered in order to identify and track coherently moving regions. The proposed clustering method uses an improved K-Means technique where K is determined using a CI (Confidence Interval) metric. We demonstrate our method in the context of a real-time traffic surveillance application.
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