Optical EngineeringProbability-based motion analysis using bidirectional prediction-independent framework in the compressed domain
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We propose a reformed method that utilizes the motion vectors (MVs) in an MPEG sequence as the motion depicter for motion analysis and representation of video contents. The MVs are converted to a uniform MV set, independent of the frame type and the direction of prediction, and then used as motion depicters in each frame. To obtain such a uniform MV set, a new motion analysis method using bidirectional prediction-independent framework is proposed. Generally, it is impossible to directly compare an I frame without MV to others such as B or P frames. But, this approach enables a frame-type-independent representation that normalizes temporal features including frame type, macroblock (MB) encoding, and MVs. Experimental results show that our method has good performance and high validity. Compared with a full-decoding method, the average of the processing time in our method is reduced about 55%, because our method is directly processed on the MPEG bit stream after variable length code (VLC) decoding. Average of the effective number of the normalized MVs in the proposed algorithm is increased about 25% than that of the conventional method.