  Anomalous activity recognition deals with identifying the patterns and events
that vary from the normal stream. In a surveillance paradigm, these events
range from abuse to fighting and road accidents to snatching, etc. Due to the
sparse occurrence of anomalous events, anomalous activity recognition from
surveillance videos is a challenging research task. The approaches reported can
be generally categorized as handcrafted and deep learning-based. Most of the
reported studies address binary classification i.e. anomaly detection from
surveillance videos. But these reported approaches did not address other
anomalous events e.g. abuse, fight, road accidents, shooting, stealing,
vandalism, and robbery, etc. from surveillance videos. Therefore, this paper
aims to provide an effective framework for the recognition of different
real-world anomalies from videos. This study provides a simple, yet effective
approach for learning spatiotemporal features using deep 3-dimensional
convolutional networks (3D ConvNets) trained on the University of Central
Florida (UCF) Crime video dataset. Firstly, the frame-level labels of the UCF
Crime dataset are provided, and then to extract anomalous spatiotemporal
features more efficiently a fine-tuned 3D ConvNets is proposed. Findings of the
proposed study are twofold 1)There exist specific, detectable, and quantifiable
features in UCF Crime video feed that associate with each other 2) Multiclass
learning can improve generalizing competencies of the 3D ConvNets by
effectively learning frame-level information of dataset and can be leveraged in
terms of better results by applying spatial augmentation.
