Abstract
Anomaly detection in crowded scenes is an important issue in computer vision. In this paper, we propose a novel framework which takes both appearance and motion characteristics into consideration to detect anomalies. A new foreground object localization method is put forward at first to extract object proposals. For motion representation, we present a novel local motion based descriptor named as Spatially Localized Multi-scale Histogram of Optical Flow (SL-MHOF) to capture the local motion statistics for each object proposal. For appearance representation, we apply convolutional neural networks (CNNs) because of their high visual discriminative capacities. These two features are then fed into Gaussian Mixture Model (GMM) Classifiers respectively to generate anomaly scores, which are fused with a softmax function to produce the final anomaly detection results. Experiments on UCSD datasets indicate the effectiveness of our proposed approach, which achieves state-of-the-art performance.
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