Thanarat H. Chalidabhongse

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We present a real-time algorithm for foreground–background segmentation. Sample background values at each pixel are quantized into codebooks which represent a compressed form of background model for a long image sequence. This allows us to capture structural background variation due to periodic-like motion over a long period of time under limited memory.(More)
We present a new fast algorithm for background modeling and subtraction. Sample background values at each pixel are quantized into codebooks which represent a compressed form of background model for a long image sequence. This allows us to capture structural background variation due to periodic-like motion over a long period of time under limited memory.(More)
Recognizing human actions is a challenging research area due to the complexity and variation of human's appearances and postures, the variation of camera settings, and angles. In this paper, we introduce a motion descriptor based on direction of optical flow for human action recognition. The directional value of a silhouette is divided into small regions.(More)
This paper presents a statistical adaptive realtime background subtraction algorithm that is very robust to moving shadows and dynamic scene environment. The algorithm enhances the previously developed method reported in [4] by adding adaptation of modeling correspond to dynamic background using adaptive brightness and color distortion. In addition, we(More)
In this paper, we develop face and hand tracking for sign language recognition system. The system is divided into two stages: the initial and tracking stages. In initial stage, we use the skin feature to localize face and hands of signer. The ellipse model on CbCr space is constructed and used to detect skin color. After the skin regions have been(More)
The Background Subtraction Algorithm has been proven to be a very effective technique for automated video surveillance applications. In statistical approach, background model is usually estimated using Gaussian model and is adaptively updated to deal with changes in dynamic scene environment. However, most algorithms update background parameters linearly.(More)
This paper proposes a method for real-time face detection and identification using two cooperative pan-tilt-zoom (PTZ) cameras. For each camera, the human face is detected and segmented using motion and skin color cues. The face segment is then analyzed by considering the relative position of the facial color blob to determine the pose. After facial pose is(More)
In this paper, we present a new stereo approach for tracking human face by using only two cameras in system. One pan-tilt camera is used for tracking person focused on face. One static camera cooperate with pan-tilt camera are used as a stereo system to estimate face 3D position. We propose to update relative position between cameras to reflect camera(More)
The Background Subtraction Algorithm has been proven to be a very effective technique for automated video surveillance applications. In statistical approach, background model is usually estimated using Gaussian model and is adaptively updated to deal with changes in dynamic scene environment. However, most algorithms update background parameters linearly.(More)
Presently researches in networked surveillance system grow continuously and substantially. One reason is because of the insecurity incidents such as terrorism acts in Thailand and many countries around the world. This results in the need of intelligent surveillance and monitoring system consisting of real-time image capture, transmission, processing, and(More)