Towards Automatic Segmentation of Semantic Video Objects

It remains a challenge to automatically segment video objects consistent with human visual perception and content understanding. Based on extensive literature survey on existing video object segmentation algorithms, we propose in this paper an integrated approach for semantic video object segmentation, where shape cues in successive frames is taken into account in exploiting the principle of change detection together with pre- and post processing to improve its segmentation accuracy. In comparison with existing segmentation techniques, our contribution can be highlighted as: (i) pre-processing via image sharpness to present a strong edge detection and improve change detections; (ii) localizing the positional range of the moving object to reduce the computation followed by the step of getting the difference of these two frames in that position range to extract the moving object mask. Benchmarked by one of the well reported existing algorithms, we illustrate that our proposed algorithm achieves certain level of improvements, and our algorithm also features in automatic semantic object segmentation inside videos