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Conventional shot boundary detection (SBD) algorithms have limitations in handling video data that contain fast illumination changes or rapid motions of objects and background. This paper presents a SBD algorithm that is efficient for action movies/videos. A measure of discontinuity in camera and object/background motion is proposed for SBD based on the combination of two motion features: the modified displaced frame difference (DFD) and the blockwise motion similarity. A variant of the block matching algorithm (BMA) is developed for the former and we find that it is a robust SBD feature for fast motion video. The latter measures the similarity of motion directions of small local region such as macroblock (MB). Experimental results with seven test action movies show the effectiveness of the proposed algorithm. The proposed features can be easily applied to compressed video. [ABSTRACT FROM AUTHOR]

In this paper, we present a gesture recognition system for an interaction between a human being and a robot. To recognize human gesture, we use a hidden Markov model (HMM) which takes a continuous stream as an input and can automatically segments and recognizes human gestures. The proposed system is composed of three modules: a pose extractor, a gesture recognizer, and a robot controller. The pose extractor replaces an input frame by a pose symbol. In this system, a pose represents the position of user's face and hands. Thereafter the gesture recognizer recognizes a gesture using a HMM, which performs both segmentation and recognition of the human gesture simultaneously [6]. Finally, the robot controller handles the robot as transforming the recognized gesture into robot commands. To assess the validity of the proposed system, we used the proposed recognition system as an interface to control robots, RCB-1 robot. The experimental results verify the feasibility and validity of the proposed system. [ABSTRACT FROM AUTHOR]