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Object tracking is the cornerstone of many visual analytics systems. Whileconsiderable progress has been made in this area in recent years, robust,efficient, and accurate tracking in real-world video remains a challenge. Inthis paper, we present a hybrid tracker that leverages motion information fromthe compressed video stream and a general-purpose semantic object detectoracting on decoded frames to construct a fast and efficient tracking engine. Theproposed approach is compared with several well-known recent trackers on theOTB tracking dataset. The results indicate advantages of the proposed method interms of speed and/or accuracy.Other desirable features of the proposed methodare its simplicity and deployment efficiency, which stems from the fact that itreuses the resources and information that may already exist in the system forother reasons.

MV-YOLO: Motion Vector-aided Tracking by Semantic Object Detection