无需跟踪的场景事件识别(英文)

Scene Event Recognition Without Tracking

提出了一种用于视觉监控中行为识别的新颖方法 .该方法将相应于目标行为的场景事件建模为一组使用PCH (PixelChangeHistories)检测的自治像素级事件 .结合基于改进的MDL(MinimumDescriptionLength)的自动模型规则选择 ,EM (Expectation Maximisation)算法被采用来聚类这些像素级的自治事件成为语义上更有意义的区域级的场景事件 .该方法是计算上有效的 。

We present a novel approach to behaviour recognition in visual surveillance under which scene events corresponding to object behaviours are modelled as groups of affiliated autonomous pixel-level events automatically detected using Pixel Change Histories (PCHs). The Expectation-Maximisation (EM) algorithm is employed to cluster these pixel-level events into semantically more meaningful blob-level scene events, with automatic model order selection using modified Minimum Description Length (MDL). The method is computationally efficient allowing for real-time performance. Experiments are presented to demonstrate the effectiveness of recognising these scene events without object trajectory matching.