Weakly Supervised Video Anomaly Detection Combining Deep Features with Shallow Neural Networks

Abstract

Deep features have outgrown hand-sketched features in many applications. The availability of pre-trained deep feature extractors helps to overcome one of the deep learning main drawbacks, which is the need for large volumes of data for training. Multiple Instance Learning (MIL) has become an attractive solution for video surveillance literature once it allows working with weakly labeled bases. This work evaluates a video anomaly detection approach based on the MIL paradigm combining deep features with shallow Neural Networks. For computational efficiency, we apply Principal Component Analysis (PCA) for dimensionality reduction before classification. We performed the experiments from a set of I3D (Inflated 3D) features, which corresponds to the ShanghaiTech benchmark dataset, and the MLP and SVM shallow classifiers achieved competitive results.