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Shoplifting or Theft Detection

Ensure prevention of Shoplifting, employee theft, minimize insurance loss and other related damages across the retail sector.

Shoplifting detection

Detection of shoplifting or theft event


Typically considered one of the most accessible and in many cases least-sophisticated types of crime, shoplifting persists as an undeniably damaging affliction across the retail sector. In fact, the National Retail Security Survey reported that loss of inventory cost U.S. retailers an estimated $49 billion USD in 2016, with 70 percent of the loss caused by employee theft and shoplifting.

Theft or shoplifting detection models can provide businesses with a proactive approach to preventing losses due to theft or shoplifting, promoting employee safety, complying with legal requirements, and deterring potential offenders.

There are several reasons why theft or shoplifting detection models are necessary at workplaces:

  • Loss prevention: Theft or shoplifting can result in significant financial losses for businesses. By implementing theft or shoplifting detection models, businesses can identify and prevent such losses.

  • Employee safety: Theft or shoplifting incidents can also put employees at risk, especially if they attempt to intervene. Detection models can provide a safer way to monitor and prevent such incidents.

  • Legal compliance: Some industries are required by law to implement security measures to prevent theft or shoplifting. Implementing a detection model can help businesses comply with these regulations.

  • Deterrent effect: The presence of a theft or shoplifting detection model can act as a deterrent to potential offenders, reducing the likelihood of theft or shoplifting incidents.

VisionAI Based Monitoring

Theft or shoplifting detection using our solution can prove to be an important application in retail settings, as it can help to prevent loss and increase security.

To detect theft or shoplifting our model is trained on a dataset of video footage with labeled instances of theft or shoplifting. The state-of-the-art model is then learned, to recognize patterns in the video data that are associated with suspicious behavior, such as loitering near a display or concealing merchandise in a bag or pocket.

Our trained model can be used to analyze live video footage from surveillance cameras in real-time. The system can alert security personnel or trigger an alarm when it detects suspicious behavior, allowing them to intervene and prevent the theft or shoplifting from occurring. Our systems for theft or shoplifting detection uses various techniques, such as object detection, tracking, and activity recognition. These techniques can be combined to create a more robust and accurate system.

Model card

Dataset size Version Camera support Precision Recall mAP
1280 v1 Both(Ceiling and Straight) 95.0% 91.6% 88.0%

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