Shoplifting Prevention

Prevention of thefts in retail shops

Desktop, MLaaS, Web

Objectives and challenges

Shoplift is a persisting social and criminal issue that costs retail business owners significant sums of money over the course of their activities. Identifying, pursuing and keeping perpetrators accountable is a current way to handle the issue. However, a more efficient way is to spot shoplifters beforehand to prevent thefts. SOLVVE team took on the development of such prevention tools using machine learning to detect shoplifting on existing systems of surveillance cameras instead of high-resolution ones.

Solutions

The key point is using video footage from surveillance cameras in shops, supermarkets, trade centers, and other public facilities to detect the anomalies in the human behavior and classify anomalies as shoplifting, robbery, stealing, etc. as compared to regular shoppers’ behavior. SOLVVE team used CNN i3d with transfer learning and fine-tuning techniques along with UCFCrime dataset of videos catching anomalies in human behavior to train the model that can detect such anomalies in real time and send signals about the shoplifting actions to those in charge of security and crime prevention.

Features

  • Monitoring human behaviour in its richness in real time to spot anomalies.
  • Comparing observed behaviour to a plethora of known behavioural patterns classified as theft, robbery, stealing, etc.
  • Automatically alarming security when suspecting a shoplifting act.

Results

  1. Successfully identifying anomalies in human behaviour identified as shoplifting.
  2. Classifying results of detection into types of anomalies: shoplifting, stealing, robbery, etc.
  3. Ensuring protection of retail business from financial loss caused by shoplifting.