Moving Object Detection in HEVC Video by Frame Sub-sampling

Video compression aims to remove spatial-temporal redundancies where the encoded bitstream, particularly the motion vectors, may not represent the actual motions in the video. Hence, moving object detection in the compressed video stream is a technically challenging task. In this work, we propose a novel moving object detection algorithm using frame sub-sampling method in the state-of-the-art HEVC video coding standard. Specifically, the number of frames is reduced by means of (temporal) sub-sampling. The frames are re-encoded using HEVC with the same environmental setting to amplify the motion of the moving objects. Sub-sampling effectively increases the motion intensity of the objects, which can be the significant cue for detecting moving object while motions in the background still remain small. Motion vectors and INTRA coding units of moving object obtained via frame sub-sampling and re-encoding are selectively utilized to separate the background and moving objects in the video. The segmented results are refined and compared with the result without performing frame sub-sampling. Results show that the sub-sampling method achieves higher accuracy, with an improvement greater than 0.35 in terms of F-measure.