LAUSR

Abstract In this paper, a weakly supervised framework is proposed for Abnormal Behavior Detection and Localization (ABDL) in the scenes. First, the objects in the scene such as pedestrians, vehicles, etc. are detected using the Faster Regional Conventional Neural Network (Faster R-CNN); then, the object behavior is described by a Histogram of Large Scale Optical Flow (HLSOF) descriptor; finally, the Multiple Instance Support Vector Machine (MISVM) is trained and then used to identify the testing behaviors as normal or abnormal. Summarily, the proposed approach has three main advantages: (1) Benefit from the Faster R-CNN, our approach can analyze the behavior at object-wise, which makes our approach has good generality and high computational efficiency; (2) The HLSOF descriptor can characterize the object behavior efficiently, and is insensitive to the variations of the size of objects; (3) As a weakly supervised learning framework, the MISVM only requires the labels at the bag level rather than instance level, which makes our approach has high accuracy as the supervised approaches but not requires completely labeled training samples, only the frame-level label is required. Experimental results analysis on different datasets validates the effectiveness of our approach.