Spatio-Temporal object recognition

Abstract

Object recognition in video is in most cases solved by extracting keyframes from the video and then applying still image recognition methods on these keyframes only. This procedure largely ignores the temporal dimension. Nevertheless, the way an object moves may hold valuable information on its class. Therefore, in this work, we analyze the effectiveness of different motion descriptors, originally developed for action recognition, in the context of action-invariant object recognition. We conclude that a higher classification accuracy can be obtained when motion descriptors (specifically, HOG and MBH around trajectories) are used in combination with standard static descriptors extracted from keyframes. Since currently no suitable dataset for this problem exists, we introduce two new datasets and make them publicly available.