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COMPRESSED-DOMAIN OBJECT DETECTION FOR VIDEO UNDERSTANDING

In this paper, a novel algorithm for the real-time, unsupervised object detection in compressed-domain sequences is proposed. The algorithm utilizes color and motion information present in the compressed stream as well as a simple object model. Extraction of the MPEG-7 dominant color descriptor, clustering of macroblocks to dominant color clusters and model-based cluster selection are employed for object detection in I-frames, while temporal tracking is employed for P-frames. The proposed methodology assumes neither a static camera nor that there exists a single dominant color in the frame, which represents the object of interest. Experimental results of road detection in various sequences demonstrate the efficiency of the proposed approach and reveal the potential of employing it for video understanding and semantic information extraction in contextspecific applications.