1 research outputs found
Tracking Passengers and Baggage Items using Multi-camera Systems at Security Checkpoints
We introduce a novel tracking-by-detection framework to track multiple
objects in overhead camera videos for airport checkpoint security scenarios
where targets correspond to passengers and their baggage items. Our approach
improves object detection by employing a test-time data augmentation procedure
that provides multiple geometrically transformed images as inputs to a
convolutional neural network. We cluster the multiple detections generated by
the network using the mean-shift algorithm. The multiple hypothesis tracking
algorithm then keeps track of the temporal identifiers of the targets based on
the cluster centroids. Our method also incorporates a trajectory association
mechanism to maintain the consistency of the temporal identifiers as passengers
travel across camera views. Finally, we also introduce a simple distance-based
matching mechanism to associate passengers with their luggage. An evaluation of
detection, tracking, and association performances on videos obtained from
multiple overhead cameras in a realistic airport checkpoint environment
demonstrates the effectiveness of the proposed approach.Comment: 14 pages, 11 figure