Theft Detection
An AI-powered theft detection system built on computer vision, deep learning and pose estimation to analyse human behaviour in real time. The system tracks body keypoints and movement patterns, learns what normal activity looks like, and automatically flags anomalous actions that may indicate theft or unauthorised activity.
- Technology
- PythonComputer VisionPose EstimationAnomaly Detection
- Key capabilities
- Skeletal keypoint tracking / Normal-activity behaviour modelling / Real-time anomaly flagging / Multi-person handling in crowded scenes / Runs on existing surveillance feeds
See it running
Recorded from the live buildAbout the product
Rather than watching pixels move, the system reads posture. Pose estimation resolves each person into a skeleton of tracked keypoints, and movement patterns across those keypoints are modelled to establish a baseline of normal activity for the scene.
Deviations from that baseline surface as anomalies in real time. The architecture is scalable and performance-focused, which is what makes intelligent real-time surveillance and behavioural analysis practical on live feeds.
The challenge
Traditional surveillance depends on manual monitoring, which makes accurate, timely theft detection close to impossible at scale.
- Operators cannot watch every feed continuously, so incidents are found after the fact
- Conventional motion detection cannot separate suspicious behaviour from ordinary movement
- Crowded and dynamic environments defeat naive pixel-difference approaches
- Identifying intent requires analysing human movement patterns, not just presence
- Real-time performance required on existing camera infrastructure
What it demonstrates
The system strengthened security monitoring by detecting suspicious activity and behavioural anomalies as they happened.
- Accurate real-time detection of suspicious activity
- Surveillance analysis automated, reducing dependence on manual observation
- Improved operational efficiency for monitoring staff
- Higher response accuracy on flagged incidents
- A reliable foundation for wider intelligent security and monitoring work
More work
All case studies
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