# Suspicious Behavior Detection on Shoplifting Cases for Crime Prevention by Using 3D Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2005.02142 · HTML: https://mlsanity.com/paper/2005.02142/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/2408.00792/) (2024)
- [Empirical and Experimental Insights into Data Mining Techniques for Crime Prediction: A Comprehensive Survey](https://mlsanity.com/paper/2403.00780/) (2024)

## Abstract (arXiv)

Crime generates significant losses, both human and economic. Every year, billions of dollars are lost due to attacks, crimes, and scams. Surveillance video camera networks are generating vast amounts of data, and the surveillance staff can not process all the information in real-time. The human sight has its limitations, where the visual focus is among the most critical ones when dealing with surveillance. A crime can occur in a different screen segment or on a distinct monitor, and the staff may not notice it. Our proposal focuses on shoplifting crimes by analyzing special situations that an average person will consider as typical conditions, but may lead to a crime. While other approaches identify the crime itself, we instead model suspicious behavior -- the one that may occur before a person commits a crime -- by detecting precise segments of a video with a high probability to contain a shoplifting crime. By doing so, we provide the staff with more opportunities to act and prevent crime. We implemented a 3DCNN model as a video feature extractor and tested its performance on a dataset composed of daily-action and shoplifting samples. The results are encouraging since it correctly identifies 75% of the cases where a crime is about to happen.