# Anomaly Recognition from surveillance videos using 3D Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2101.01073 · HTML: https://mlsanity.com/paper/2101.01073/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks](https://mlsanity.com/paper/1706.00527/) (2017)
- [Abnormal Event Detection in Videos using Spatiotemporal Autoencoder](https://mlsanity.com/paper/1701.01546/) (2017)
- [Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes](https://mlsanity.com/paper/1609.00866/) (2016)
- [Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos](https://mlsanity.com/paper/1703.10664/) (2017)
- [Motion-Aware Feature for Improved Video Anomaly Detection](https://mlsanity.com/paper/1907.10211/) (2019)
- [CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis](https://mlsanity.com/paper/1809.05782/) (2018)
- [What Actions are Needed for Understanding Human Actions in Videos?](https://mlsanity.com/paper/1708.02696/) (2017)
- [PiiGAN: Generative Adversarial Networks for Pluralistic Image Inpainting](https://mlsanity.com/paper/1912.01834/) (2019)

## Abstract (arXiv)

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.