Finding the Accurate Natural Contour of Non-rigid Objects in Video

Abstract

Non-rigid object tracking is an important task in computer vision, while its natural contour extraction is one of the most difficult problems during the process. Most tracking-by-detection methods are based on rectangular bounding-boxes, this will lead errors into subsequent detection. This paper present a novel superpixel-based detector for accurate natural contour extraction, there are three main contributions: 1) combining real-time superpixel segmentation with natural contour detection, 2) proposing an object-oriented natural contour extraction method for non-rigid objects, 3) proposing a non-rigid object detection method based on flexible scanning window. Compared with those bounding-box based detection methods, our detector can provide very accurate initial input of object model, then produce accurate natural contour output of the non-rigid object. Our detector broke the conventional detection method based on scanning rectangle, which greatly reduced the interference caused by background information. The experiments show that the proposed method outperforms the state-of-the-art algorithms not only on the contour accuracy but also on the computation cost. In addition, the initialization stage of our method overcomes the limitation of HT caused by the size of initial bounding-box.

This work was supported by the National Natural Science Foundation of China (NSFC-60573123,60605013,60870002, 60802087), NCET, and the Science and Technology Dept of Zhejiang Province (2012R10052,Y1110688).