Video Abnormal Event Detection Based on CNN and Multiple Instance Learning

Aiming at the need of video abnormal events to be located in pixel-level regions, a video abnormal event detection method based on CNN (Convolutional Neural Networks) and multiple instance learning is proposed. Firstly, the Gaussian background model is used to extract the moving targets in the video, and the connected regions of the moving targets are obtained by the image processing method. Secondly, the pre-trained VGG16 model is used to extract the features of the connected regions what construct multiple instance learning packages. Finally, the multiple instance learning model is trained using MISVM (Multiple-Instance Support Vector Machines) and NSK (Normalized Set Kernel) algorithms and predicted at the pixel-level. The experimental results show that the video anomaly detection method based on CNN and multiple instance learning can accurately locate the abnormal events in the pixel-level region.