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Sign language spotting is the task of detection and recognition of signs (words in the predefined vocabulary) and fingerspellings (a combination of continuous alphabets that are not found in signs) in a signed utterance. The internal structures of signs and fingerspellings differ significantly. Therefore, it is difficult to spot signs and fingerspellings simultaneously. In this paper, a novel method...
Vision-based human action recognition provides an advanced interface, and research in the field of human action recognition has been actively carried out. However, an environment from dynamic viewpoint, where we can be in any position, any direction, etc., must be considered in our living 3D space. In order to overcome the viewpoint dependency, we propose a Volume Motion Template(VMT) and Projected...
In this paper, we propose a spatio-temporal silhouette representation, called silhouette energy image (SEI), and variability models, to characterize motion and shape properties for automatic recognition of human actions in daily life. To address the variability in the recognition of human actions, several parameters, such as anthropometry of the person, speed of the action, phase (starting and ending...
In this paper, we describe a dynamic Bayesian network or DBN based approach to both two-hand gestures and one-hand gestures. Unlike wired glove-based approaches, the success of camera-based methods depends greatly on image processing and feature extraction results. So the proposed method of DBN-based inference is preceded by fail-safe steps of motion tracking. Then a new gesture recognition model...
Player's gesture and action spotting in sports video is a key task in automatic analysis of the video material at a high level. In many sports views, the camera covers a large part of the sports arena, so that the area of player's region is small, and has large motion. These make the determination of the player's gestures and actions a challenging task. To overcome these problems, we propose a method...
The representation of gestures changes dynamically, depending on camera viewpoints. This camera viewpoints problem is difficult to solve in environments with a single directional camera, since the shape and motion information for representing gestures is different at different viewpoints. In view-based methods, data for each viewpoint is required, which is ineffective and ambiguous in recognizing...
In this paper, we propose a method for human tracking using 3D human body model in a video sequence with a monocular moving camera. Tracking a human with unconstrained movement in moving monocular camera image sequence is extremely challenging. Our 3D human body model which is formed with articulation model of hierarchical tree structure can express all human's movement by parameters. We can obtain...
The spotting and recognition of the human gestures is a key task in automating the analysis of the video material and human-robot interaction. Specially applying this technology to low-resolution video has many potential applications. The human area is small with respect to input video frames in broadcast sports video, surveillance video, etc. However, this condition makes the spotting certain gesture...
Robust gesture recognition in video requires segmentation of the meaningful gestures from a whole body gesture sequence. This is a challenging problem because it is not straightforward to describe and model meaningless gesture patterns. This paper presents a new method for simultaneous spotting and recognition of whole body key gestures. A human subject is first described by a set of features encoding...
This paper presents a full-body gesture database which contains 2D video data and 3D motion data of 14 normal gestures, 10 abnormal gestures and 30 command gestures for 20 subjects. We call this database the Korea University Gesture (KUG) database. Using 3D motion cameras and 3 sets of stereo cameras, we captured 3D motion data and 3 pairs of stereo-video data at 3 different directions for normal...
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