  In this paper, we propose a weakly supervised deep temporal encoding-decoding
solution for anomaly detection in surveillance videos using multiple instance
learning. The proposed approach uses both abnormal and normal video clips
during the training phase which is developed in the multiple instance framework
where we treat video as a bag and video clips as instances in the bag. Our main
contribution lies in the proposed novel approach to consider temporal relations
between video instances. We deal with video instances (clips) as a sequential
visual data rather than independent instances. We employ a deep temporal and
encoder network that is designed to capture spatial-temporal evolution of video
instances over time. We also propose a new loss function that is smoother than
similar loss functions recently presented in the computer vision literature,
and therefore; enjoys faster convergence and improved tolerance to local minima
during the training phase. The proposed temporal encoding-decoding approach
with modified loss is benchmarked against the state-of-the-art in simulation
studies. The results show that the proposed method performs similar to or
better than the state-of-the-art solutions for anomaly detection in video
surveillance applications.
