Descritor de movimento baseado em tensor e histograma de gradientes

Abstract

The motion pattern recognition has become a very attractive research field in recent years due to the large amount of video data and the creation of human-machine interfaces that use facial and body expressions. This field can be considered one of the key requirements for analysis and understanding in video. This thesis proposes a motion descriptor based on second order tensor and histograms of oriented gradients. The calculation of the descriptor is fast, simple and effective. Furthermore, no prior knowledge of data basis is required and the addition of new classes of motion and videos do not need to recalculate the existing descriptors. The frame of a video is divided into a grid and the histogram of oriented gradients is computed in each cell. After that, the frame tensor is computed and the final descriptor is built by a series of frame tensors. The descriptor is evaluated in both KTH and Hollywood2 data basis, used in the current literature, with a Support Vector Machine classifier (SVM). The results obtained on the basis KTH are very close to the descriptors of the state-of-the-art that use local information of the video. The results obtained on the basis Hollywood2 not outweigh the state-of-the-art but are close enough to conclude that the proposed method is effective. Although the literature presents descriptors that have superior results, their approaches are complex and with computational cost.