Robust Localization of Body Parts Based on Interframe Failure Correction
Shingo Kobayashi, Hiroyuki Kaseda, Ryusuke Miyamoto · 2018
Localization of body parts in image sequences is beginnings to become practical owing to the improvement in accuracy of image recognition by deep learning. Sports scene analysis is one application of localization of body parts, but existing schemes have difficulty obtaining sufficient accuracy because a sports scene includes variegated patterns of poses and occlusions that decrease the robustness of localization. To robustly localize body parts in sports scenes, this paper proposes a novel scheme that applies failure correction using interframe information to extend an existing scheme proposed by Cao et al. Experimental results using a novel dataset composed of image sequences of a tennis player showed that the proposed scheme reduced the occurrence rate of failure frames to 0% from 36.7% for the existing scheme.