Titlebook: Computer Vision -- ACCV 2010 Workshops; ACCV 2010 Internatio Reinhard Koch,Fay Huang Conference proceedings 2011 Springer-Verlag GmbH Berli

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https://doi.org/10.1057/9781137062079ation method by introducing a novel bi-directional vector-based zero comparison to enhance the accuracy of noisy motion vector elimination, and we propose an efficient algorithm for zero comparison. We demonstrate the effectiveness of the proposed method through several experiments using actual video data acquired by an MPEG video camera.

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Ellen A. Skinner,Melanie J. Zimmer-Gembeckram comparison. The proposed probabilistic indexing technique, together with the two robust measurements, greatly increases the discriminative power of the histogram representation. Both qualitative and quantitative evaluations show the robustness of the proposed approach against partial occlusion, noisy and clutter background.

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Multicamera Video Summarization from Optimal Reconstructionrm the summary of another. The results show that this approach can significantly reduce or even eliminate the inclusion of patches in the summary that contain activities from the video that are already expected based on other summary patches, leading to a more concise output.

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Noisy Motion Vector Elimination by Bi-directional Vector-Based Zero Comparisonation method by introducing a novel bi-directional vector-based zero comparison to enhance the accuracy of noisy motion vector elimination, and we propose an efficient algorithm for zero comparison. We demonstrate the effectiveness of the proposed method through several experiments using actual video data acquired by an MPEG video camera.

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Spatio-Temporal Optimization for Foreground/Background Segmentationhen used to determine accurate silhouettes of the foreground..We demonstrate the effectiveness of our approach based on a qualitative data analysis and compare it to other state of the art approaches.

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Interactive Motion Analysis for Video Surveillance and Long Term Scene Monitoringveillance system. Quantitative performance evaluation is performed on a variety of sequences, including standard datasets. With the two feedback mechanisms enabled together, significant improvement in tracking performance are demonstrated particularly in handling slow moving and stopped objects.