DCT-Based Object Tracking in Compressed Video

This paper presents a novel real-time DCT-based object tracking algorithm that operates on I frames in compressed MPEG video. Discrete cosine transform (DCT) coefficients that are provided from video sequences are exploited to find motion and color cues. The temporal motion and spatial color information are then fused by means of a posterior probability framework which allows the information from different measurement sources to be fused in a principled manner. The DCT-based method and probabilistic framework ensure robustness with respect to noise, occlusion, local scene changes, global illumination changes and inconsistent movement. The tracker is also able to recognize the object when it re-appears after it has left the scene. After tracker selection, the background and object model are selectively updated to adapt to environmental changes. Experimental results show the effectiveness and efficacy of the proposed method