Industrial Technology Research Institute

Wen-Hao Wang

Tai-Hui Huang

Jeongnam Youn

Chia-Wen Lin

Ming-Ting Sun

Tsung-Chan Li

A moving object detection method and an image processing system thereof are provided. First, a pixel-wise distance of a received image to a reference image is computed to obtain a distance map. A histogram analysis is performed on the distance map to obtain a distance distribution. An entropy value of the distance distribution is computed and a peak distance value which is with a maximum occurrence probability in the distance distribution is searched out. Then, by using a mapping rule, the entropy value and the peak distance value are transformed into a decision threshold value. The decision threshold value is applied in classifying the pixels of the distance map into a group of foreground attributes and a group of background attributes and thereby moving objects in the current image are obtained.

Moving object detection method and image processing system for moving object detection

A foreground image separation method is disclosed to separate dynamic foreground and static background in a sequence of input images which have been processed either with automatic white balance or brightness control by the camera. First, an input image is received from the camera, and then a white balance or brightness compensation is performed to the input image according to a reference image to generate a compensated image with background color and background brightness which are approximately similar to that of the reference image. Finally, a background subtraction algorithm is performed to the compensated image to generate a background separation result. The background subtraction algorithm could be a Gaussian Mixture Model based algorithm. The method could process successive images received from the camera to continuously generate background separation results and update the reference image accordingly, such that video surveillance system could adapt to the change of illumination.

Foreground image separation method

Disclosed is an object detection method and system in an image plane. A Hidden Markov Model (HMM) is employed and its associated parameters are initialized for an image plane. Updating HMM parameters is accomplished by referring to the previous estimated object mask in a spatial domain. With the updated HMM parameters and a decoding algorithm, a refined state sequence is obtained and a better object mask is restored from the refined state sequence. Consequently, estimation of the HMM parameters can be rapidly achieved and robust object detection can be effected. This allows the resultant object mask to be closer to the real object area, and the false detection in the background area can be decreased.

Method and system for object detection in an image plane

A transcoder for transcoding digital video signals includes a decoder and an encoder. In the decoder, an end-of-block (EOB) position of an incoming block received by the decoder is determined and a discrete cosine transform (DCT) block type is determined based on the determined EOB position. A reduced number of DCT coefficients is computed in a subsequent inverse DCT computation based on the DCT block type. In the encoder, if the incoming block is intercoded, no DCT coefficients are computed after the EOB of the incoming blocks is performing a DCT. Further, in the encoder when the incoming block is intercoded, an algorithm is applied to predict which DCT coefficients may become zero after a subsequent quantization operation, and only DCT coefficients that may not become zero are computed in performing the DCT.

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Hsinchu, Taiwan

Wen-Hao Wang