A CCTV provider needed help detecting shoplifting. Learn how Aya created a real-time system for detecting theft and warning behaviors.
Shoplifting is a major issue, with over 200 million incidents reported annually in the U.S. alone—equating to more than 500,000 cases a day. Globally, shoplifting results in over $120 billion in losses each year. To combat this problem, many companies are turning to technology for innovative solutions. A leading CCTV analytics provider sought to develop a model that could detect shoplifting in real-time, and they needed precise labelling of shoplifting events for training this model.
Aya Data was tasked with labelling 5,000 hours of CCTV footage to identify shoplifting events. This involved:
– Labelling Individuals: Each person in every frame of the footage had to be labelled with precise bounding boxes.
– Marking Theft Events: Specific key points were added to indicate the start and end of thefts.
The footage presented challenges such as poor quality, non-sequential events from various stores, and sub-optimal camera angles, making accurate labelling and tracking difficult.
Aya Data tackled these challenges by:
1. Pre-Processing the Footage: We divided the 5,000 hours of CCTV footage into manageable segments.
2. Experienced Annotation Team: Over 100 skilled video annotators labelled each frame, identifying individuals and marking theft events.
3. Quality Assurance: Each frame was manually annotated and reviewed by a quality assurance lead to ensure accuracy before being delivered to the client.
The meticulously labelled footage was used to train a custom computer vision model that successfully detects shoplifting and warns of suspicious behaviour. The client was fully satisfied with the accuracy and effectiveness of the solution.
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Disclaimer: Aya Data respects client confidentiality and will not disclose any specific client details or project information. Any identifying information in our case studies may be anonymized.