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. The proposed study extracted 3D features by providing frame level information and spatial augmentation to a fine-tuned pre-trained model, namely 3DConvNets. Besides, the learned features are compact enough and the proposed approach outperforms significantly from state of art approaches in terms of accuracy on anomalous activity recognition having 82% AUC. 

1. ·         The difference between normal and anomaly behavior is ambiguous and under realistic conditions same behavior could be normal or anomalous such as a crowed event spotted anywhere may sometimes considered as normal or anomalous.

2. ·         As anomaly is arises due to malicious activity, these actions accommodate themselves to appear as normal so the task of tagging normal regions become more difficult.

3. ·         It is to indicate an activity that differ from normal pattern in cameras and detect the occurrence time of anomaly.

4. ·         Lack of annotated/tagged training data make it hard to identify normal and anomaly patterns for a detection system to learn it better.

5. ·         Often data is noisy, and this noise considered as anomaly and most of the time it is difficult to remove noise or differentiate between noise and anomaly.

1. Anomaly detection in surveillance videos is found to be the most challenging and long-standing problem in Computer Vision. For video surveillance applications, there are several attempts made to detect violence or aggression in videos. The most critical tasks in surveillance videos are activity recognition through different methodologies.

2.   Surveillance cameras are increasingly being used in public places that capture a variety of realistic anomalous events, but the monitoring capability of law enforcement agencies has not kept pace. Most of the time there is no police officer to look at those videos so often when an “unusual” event happens, and no one is watching that so there must be an algorithm for attention mechanism, therefore our proposed method will flag those anomalies for officers so that they can take actions whenever anomaly happens.

3.  To lessen the effort of labor and time, designing a computer aided system or algorithm to detect anomalies in videos is becoming a pressing need.