Semi-supervised Anomaly Detection for Weakly-annotated Videos

One of the significant challenges in surveillance anomaly detection research is the scarcity of surveillance datasets satisfying specific ethical and logistical requirements during the collection process. Weakly supervised models aim to solve those challenges by only weakly annotating surveillance videos and creating sophisticated learning techniques to optimize these models, such as Multiple Instance Learning (MIL), which maximizes the boundary between the most anomalous video clip and the least normal (false alarm) video clip using ranking loss. However, maximizing the boundary does not necessarily assign each clip its correct class. We propose a semi-supervision technique that creates pseudo labels for each correct class. Also, we investigate different video recognition models for better features representation. We evaluate our work on the UCF-Crime (Weakly Supervised) dataset and show that it almost outperforms all other approaches by only using the same simple baseline (multilayer perceptron neural network). Moreover, we incorporate different evaluation metrics to show that not only did our solution increase the AUC, but it also increased the top-1 accuracy drastically.