Fast abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on the inherent redundancy of video structures, we propose an efficient sparse combination learning framework with both batch and online solvers. It achieves decent performance in the detection phase without compromising result quality. The extremely fast execution speed is guaranteed owing to the fact that our method effectively turns the original complicated problem into a few small-scale least square optimizations. Our method reaches high detection rates on benchmark datasets at a speed of 1000–1200 frames per second on average when computing on an ordinary single core desktop PC using MATLAB.

中文翻译：

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快速异常事件检测

快速异常事件检测满足了处理海量监控视频日益增长的需求。基于视频结构的固有冗余，我们提出了一种高效的稀疏组合学习框架，具有批处理和在线求解器。它在检测阶段实现了不错的性能，而不会影响结果质量。由于我们的方法有效地将原始复杂问题转化为一些小规模的最小二乘优化，因此保证了极快的执行速度。当使用 MATLAB 在普通单核台式 PC 上计算时，我们的方法在基准数据集上以平均每秒 1000-1200 帧的速度达到高检测率。