This paper proposes an efficient technique for human motion recognition based on motion history images and an eigenspace technique. In recent years, human motion recognition has become one of the most popular research fields. It is expected to be applied in a security system, man-machine communication, and so on. In the proposed technique, we use two feature images and the eigenspace technique to realize high-speed recognition. An experiment was performed on recognizing six human motions and the results showed satisfactory performance of the technique.

This paper introduces a simple algorithm for pedestrian detection on low resolution images. The main objective is to create a successful means for real-time pedestrian detection. While the framework of the system consists of edge orientations combined with the local binary patterns (LBP) feature extractor, a novel way of selecting the threshold is introduced. Using the mean-variance of the background examples this threshold improves significantly the detection rate as well as the processing time. Furthermore, it makes the system robust to uniformly cluttered backgrounds, noise and light variations. The test data is the INRIA pedestrian dataset and for the classification, a support vector machine with a radial basis function (RBF) kernel is used. The system performs at state-of-the-art detection rates while being intuitive as well as very fast which leaves sufficient processing time for further operations such as tracking and danger estimation.

Techniques for human-motion recovery are applicable to a variety of areas, such as sports, dancing, virtual reality, and video-game production. The people who work in this area focus their attention on recovering information on the motion of individuals rather than groups of people. It is important to demonstrate the possibility of recovering descriptions of the 3-D motion in team sports, since such information is able to provide us with a variety of information on the relations among players. This paper presents a new experimental result on 3-D motion recovery from a team sport. The result was obtained by a non-rigid shape recovery technique based on images from uncalibrated cameras. The technique was applied to recovering the 3-D motion of the players in a mini-basketball game which was played in a gymnasium. Some attention is focused on the analysis of the players' motion. Satisfactory results were obtained.