الفهرس 
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Abstract
Almost all public places now rely on surveillance cameras to increase public safety. However, the human need for surveillance analysis is very high in demand and very costly. The need for automatic surveillance anomaly detection systems is now higher than ever. One of the significant challenges in the research and development of surveillance anomaly detection systems is the scarcity of available datasets due to specific ethical and logistical requirements during the collection process. Another challenge is the nature of the surveillance videos, which are by default long and diverse. Long videos mean that we must do tons of work to annotate them fully, and diverse videos suggest that we cannot apply many classical statistical-based vision algorithms. Working on anomalous videos adds insult to injury; given a surveillance video, we have to detect and recognize all abnormal segments, which naturally are rare, sparse, and tiny. Weakly supervised models aim to solve those challenges by only weakly annotating surveillance videos and creating sophisticated learning techniques to optimize these models. By doing so, we avoid the need for exhaustive data collection and annotation by instead building general learning techniques to squeeze the missing information. One of those learning techniques is the 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 label; also, the produced models suer from bias to the useless background spatiotemporal information in surveillance videos. We propose a semi-supervision learning technique on top of the weakly supervised model to create pseudo labels for each correct class and address the spatiotemporal background bias. Also, we investigate dierent video recognition models for better features representation. We evaluate our work on the UCF-Crime (Weakly-Labeled) dataset and show that it almost outperforms all other approaches by only adopting our learning technique on the most simple baseline (multilayer perceptron neural network). Moreover, we incorporate dierent evaluation metrics to show that not only did our solution increase the AUC, but it also increased the top-1 accuracy drastically.