This paper presents a novel technique for embedding a digital watermark into video frames based on motion vectors and discrete wavelet transform (DWT). In the proposed scheme, the binary image watermark is divided int...This paper presents a novel technique for embedding a digital watermark into video frames based on motion vectors and discrete wavelet transform (DWT). In the proposed scheme, the binary image watermark is divided into blocks and each watermark block is embedded several times in each selected video frame at different locations. The block-based motion estimation algorithm is used to select the video frame blocks having the greatest motion vectors magnitude. The DWT is applied to the selected frame blocks, and then, the watermark block is hidden into these blocks by modifying the coefficients of the Horizontal sub-bands (HL). Adding the watermark at different locations in the same video frame makes the scheme more robust against different types of attacks. The method was tested on different types of videos. The average peak signal to noise ratio (PSNR) and the normalized correlation (NC) are used to measure the performance of the proposed method. Experimental results show that the proposed algorithm does not affect the visual quality of video frames and the scheme is robust against a variety of attacks.展开更多
This paper presents a human detection system in a vision-based hospital surveillance environment. The system is composed of three subsystems, i.e. background segmentation subsystem （BSS）, human feature extraction su...This paper presents a human detection system in a vision-based hospital surveillance environment. The system is composed of three subsystems, i.e. background segmentation subsystem （BSS）, human feature extraction subsystem （HFES）, and human recognition subsystem （HRS）. The codebook background model is applied in the BSS, the histogram of oriented gradients （HOG） features are used in the HFES, and the support vector machine （SVM） classification is employed in the HRS. By means of the integration of these subsystems, the human detection in a vision-based hospital surveillance environment is performed. Experimental results show that the proposed system can effectively detect most of the people in hospital surveillance video sequences.展开更多
文摘This paper presents a novel technique for embedding a digital watermark into video frames based on motion vectors and discrete wavelet transform (DWT). In the proposed scheme, the binary image watermark is divided into blocks and each watermark block is embedded several times in each selected video frame at different locations. The block-based motion estimation algorithm is used to select the video frame blocks having the greatest motion vectors magnitude. The DWT is applied to the selected frame blocks, and then, the watermark block is hidden into these blocks by modifying the coefficients of the Horizontal sub-bands (HL). Adding the watermark at different locations in the same video frame makes the scheme more robust against different types of attacks. The method was tested on different types of videos. The average peak signal to noise ratio (PSNR) and the normalized correlation (NC) are used to measure the performance of the proposed method. Experimental results show that the proposed algorithm does not affect the visual quality of video frames and the scheme is robust against a variety of attacks.
基金supported by the“MOST”under Grant No.103-2221-E-468-008-MY2
文摘This paper presents a human detection system in a vision-based hospital surveillance environment. The system is composed of three subsystems, i.e. background segmentation subsystem （BSS）, human feature extraction subsystem （HFES）, and human recognition subsystem （HRS）. The codebook background model is applied in the BSS, the histogram of oriented gradients （HOG） features are used in the HFES, and the support vector machine （SVM） classification is employed in the HRS. By means of the integration of these subsystems, the human detection in a vision-based hospital surveillance environment is performed. Experimental results show that the proposed system can effectively detect most of the people in hospital surveillance video sequences.
