RESUMO
Visual surveillance produces a significant amount of raw video data that can be time consuming to browse and analyze. In this work, we present a video synopsis methodology called "scene adaptive online video synopsis via dynamic tube rearrangement using octree (SSOcT)" that can effectively condense input surveillance videos. Our method entailed summarizing the input video by analyzing scene characteristics and determining an effective spatio-temporal 3D structure for video synopsis. For this purpose, we first analyzed the attributes of each extracted tube with respect to scene geometry and complexity. Then, we adaptively grouped the tubes using an online grouping algorithm that exploits these scene characteristics. Finally, the tube groups were dynamically rearranged using the proposed octree-based algorithm that efficiently inserted and refined tubes containing high spatio-temporal movements in real time. Extensive video synopsis experimental results are provided, demonstrating the effectiveness and efficiency of our method in summarizing real-world surveillance videos with diverse scene characteristics.
RESUMO
Contour pixels distinguish objects from the background. Tracing and extracting contour pixels are widely used for smart/wearable image sensor devices, because these are simple and useful for detecting objects. In this paper, we present a novel contour-tracing algorithm for fast and accurate contour following. The proposed algorithm classifies the type of contour pixel, based on its local pattern. Then, it traces the next contour using the previous pixel's type. Therefore, it can classify the type of contour pixels as a straight line, inner corner, outer corner and inner-outer corner, and it can extract pixels of a specific contour type. Moreover, it can trace contour pixels rapidly because it can determine the local minimal path using the contour case. In addition, the proposed algorithm is capable of the compressing data of contour pixels using the representative points and inner-outer corner points, and it can accurately restore the contour image from the data. To compare the performance of the proposed algorithm to that of conventional techniques, we measure their processing time and accuracy. In the experimental results, the proposed algorithm shows better performance compared to the others. Furthermore, it can provide the compressed data of contour pixels and restore them accurately, including the inner-outer corner, which cannot be restored using conventional algorithms.
