An intelligent video analytics system that detects unattended bags and packages in public infrastructure and alerts security teams in real time.
Airports, railway stations, bus terminals, and metro stations are exposed to security risks from unattended or unidentified objects such as bags, suitcases, and packages, which may indicate theft risk or a potential hazard.
MoreYeahs built an AI-powered unattended object detection system for organizations securing this kind of public infrastructure, giving control room operators automated visibility that manual CCTV observation alone cannot provide.
Manual monitoring left security teams reactive rather than proactive:
Resource-Intensive Manual Observation: monitoring relied heavily on operators watching CCTV feeds continuously, which is resource-intensive and prone to human oversight.
Delayed Identification: unattended objects often went unnoticed for extended periods, reducing response time.
No Owner Correlation: there was no automated way to link an object to the person who left it or to track how long it had been unattended.
Scale Across Busy Facilities: high-traffic transit hubs generate more visual data than operators can reliably track.
MoreYeahs built a camera-based intelligent surveillance system that detects, classifies, and tracks objects and people in real time to flag unattended items automatically.
Object Detection and Classification: computer vision models detect and classify bags, suitcases, and packages in the camera feed.
Person–Object Association: multi-object tracking correlates each object with its associated person.
Dwell-Time Monitoring: objects that remain stationary while their owner is absent beyond a configurable threshold are flagged as unattended.
Automated Alerts: flagged events generate alerts with location and a dashboard highlight, plus a snapshot and video clip for security personnel.
The system continuously tracks the relationship between people and objects to decide when an item is genuinely unattended.
Continuous Tracking: live CCTV video is ingested and analyzed, with multi-object tracking assigning persistent IDs to people and objects.
Trajectory-Based Association: objects are associated with the nearest person based on trajectory and proximity.
Dwell-Time Escalation: when an object stays stationary and its owner moves away past the configured threshold, an alert is generated with camera ID and zone coordinates.
Exception Handling: temporary owner occlusion applies a short grace period, and objects moved by another person trigger a dwell-time reset rather than a false alert.
The solution runs on high-resolution CCTV infrastructure paired with object detection and multi-object tracking models.
In validation testing, the system met its target benchmarks for detection accuracy and alert speed.
Automated detection gives security teams at public infrastructure sites earlier, more reliable awareness of potential threats.
Faster Security Response: automated, location-tagged alerts cut the time to identify and inspect unattended items.
Reduced Operator Burden: continuous AI monitoring reduces reliance on constant manual CCTV observation.
Better Coverage at Scale: the system extends consistent monitoring across multiple zones of a busy transit facility simultaneously.
Stronger Evidence Trail: automatic snapshots and video clips support rapid verification and post-incident review.
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