Anomaly detection in traffic surveillance with sparse topic model

Abstract

Most research on anomaly detection has focused on event that is different from its spatial-temporal neighboring events. It is still a significant challenge to detect anomalies that involve multiple normal events interacting in an unusual pattern. In this work, a novel unsupervised method based on sparse topic model was proposed to capture motion patterns and detect anomalies in traffic surveillance. scale-invariant feature transform (SIFT) flow was used to improve the dense trajectory in order to extract interest points and the corresponding descriptors with less interference. For the purpose of strengthening the relationship of interest points on the same trajectory, the fisher kernel method was applied to obtain the representation of trajectory which was quantized into visual word. Then the sparse topic model was proposed to explore the latent motion patterns and achieve a sparse representation for the video scene. Finally, two anomaly detection algorithms were compared based on video clip detection and visual word analysis respectively. Experiments were conducted on QMUL Junction dataset and AVSS dataset. The results demonstrated the superior efficiency of the proposed method.

摘要

交通异常事件检测是智能交通系统的重要组成部分, 对于维护交通秩序具有不可替代的作用。 现今大多数研究都集中在检测那些与一般事件具有显著差异的异常事件, 因此很难识别由多个目标相互影响而引起的异常。 本文提出一种基于稀疏主题模型的无监督方法用于捕获监控视频中的运动模式并进行异常检测。 利用 SIFT 流对稠密轨迹进行改进, 以减少在提取兴趣点和描述符时受到的干扰。 为了获得完备的轨迹时空信息, 采用了 Fisher 核方法获得轨迹的表示并将其量化为视觉词。 随后, 提出了一种稀疏主题模型用于视频场景的分析, 不仅可以查找视频中的潜在运动模式, 同时可以实现对视频的稀疏表示。 最后, 分别从视频序列和视觉词两个方面进行异常检测。 实验在 QMUL 数据集和 AVSS 数据集上进行, 实验结果证明了本文方法的有效性。