Real-Time Activity Search of Surveillance Video

Greg Castañón, Venkatesh Saligrama, André-Louis Caron, Pierre-Marc Jodoin
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引用次数: 5

Abstract

We present a fast and flexible content-based retrieval method for surveillance video. Designing a video search robust to uncertain activity duration, high variability in object shapes and scene content is challenging. We propose a two-step approach to video search. First, local motion features are inserted into an inverted index using locality-sensitive hashing (LSH). Second, we utilize a novel optimization approach based on edit distance to minimize temporal distortion, limited obscuration and imperfect queries. This approach assembles the local features stored in the index into a video segment which matches the query video. Pre-processing of archival video is performed in real-time, and retrieval speed scales as a function of the number of matches rather than video length. We demonstrate the effectiveness of the approach for counting, motion pattern recognition and abandoned object applications using a pair of challenging video datasets.
监控视频实时活动搜索
提出了一种快速灵活的基于内容的监控视频检索方法。设计一个对不确定的活动持续时间、物体形状和场景内容的高度可变性具有鲁棒性的视频搜索是具有挑战性的。我们提出了一种两步视频搜索方法。首先,使用位置敏感散列(LSH)将局部运动特征插入到倒排索引中。其次,我们利用一种新的基于编辑距离的优化方法来最小化时间失真、有限模糊和不完美查询。该方法将存储在索引中的局部特征组合成与查询视频匹配的视频片段。档案视频的预处理是实时进行的,检索速度是匹配次数的函数而不是视频长度的函数。我们使用一对具有挑战性的视频数据集证明了该方法在计数、运动模式识别和废弃物体应用方面的有效性。
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