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This paper presents a novel robust people counting system based on fusing the depth and vision data. Conventional algorithms utilize the monoscopic or stereoscopic vision data to count people. However, these vision-based people counting methods often fail due to occasional illumination change and crowded environment. In the proposed algorithm, both the top-view vision and depth images are captured by a video-plus-depth camera mounted on the ceiling. The depth image is first processed by a morphological operator to alleviate depth artifacts such as the optical noise and lost data. Then the human object is extracted using a human model from the preprocessed depth image. Finally, the trajectory of the detected object is established by applying the bidirectional matching algorithm. Experimental results show that the proposed algorithm achieves over 98% accuracy in various testing environments. [ABSTRACT FROM PUBLISHER]

In scalable video coding (SVC), an exhaustive mode decision is performed to search for the best mode at each macroblock. Although this method achieves an optimal trade-off between rate and distortion, it introduces an extreme computational burden on the encoder. In this letter, we propose a fast mode decision algorithm that can reduce the computational load of the mode decision for SVC. We statistically derive the expectation of the rate-distortion cost (RDcost) increase caused by skipping each mode in the mode decision. In the proposed algorithm, the encoder performs the mode decision using a small number of modes that are determined based on the expected increase of the RDcost. Experimental results show that the proposed algorithm can reduce the computational complexity significantly with negligible video quality degradation and bitrate increment. [ABSTRACT FROM AUTHOR]