人の行動の表現と認識に関する研究

In recent years, analyzing human motion and recognizing a performed action from a video sequence has become very important and has been a well-researched topic in the field of computer vision. The reason behind such attention is its diverse applications in different domains like robotics, human computer interaction, video surveillance, controller-free gaming, video indexing, mixed or virtual reality, intelligent environments, etc. There are a number of researches performed on motion recognition in the last few decades. The state of the art action recognition schemes generally use a holistic or a body part based approach to represent actions. Most of the methods provide reasonable recognition results, but they are sometimes not suitable for online or real time systems because of their complexity in action representation. In this thesis, we address this issue by proposing a novel action representation scheme.The proposed action descriptor is based on a basic idea that rather than detecting the exact body parts or analyzing each action sequence, human action can be represented by a distribution of local texture patterns extracted from spatiotemporal templates. In this study, we use a novel way of generating those templates. Motion History Image (MHI) merges an action sequence into a single template. However, having the problem in overwriting old information by a new one in the MHI, we use a variant named Directional MHI (DMHI) to diffuse the action sequence into four directional templates. And then we use the Local Binary Pattern (LBP) operator, but with a unique way, a rotated bit arranged LBP, to extract the local texture patterns from those DMHI templates. These spatiotemporal patterns form the basis of our action descriptor which is formulated into a concatenated block histogram to serve as a feature vector for action recognition. However, the extracted patterns by LBP tends to lose the temporal information in a DMHI, therefore we take a linear combination of the motion history information and texture information to represent an action sequence. We also use some variants of the proposed action representation that include the shape or pose information of the action silhouettes as a form of histogram.We show that, by effective classification of such histograms, i.e., action descriptor, robust human action recognition is possible. We demonstrate the effectiveness of the proposed method along with some variants of the method over two benchmark dataset; the Weizmann dataset and KTH dataset. Our results are directly comparable or superior to the results reported over these datasets. Higher recognition rates found in the experiment suggest that, compared to complex representation, the proposed simple and compact representation can achieve robust recognition of human activity for practical use. Besides the recognition rate, due to the simplicity of the proposed technique, it is also advantageous with respect to computational load.九州工業大学博士学位論文 学位記番号:工博甲第409号 学位授与年月日:平成28年3月25日1.Introduction|2.Action Representation and Recognition|3.Experiments and Results|4.Conclusion九州工業大学平成27年