  We introduce a novel framework to track multiple objects in overhead camera
videos for airport checkpoint security scenarios where targets correspond to
passengers and their baggage items. We propose a self-supervised learning (SSL)
technique to provide the model information about instance segmentation
uncertainty from overhead images. Our SSL approach improves object detection by
employing a test-time data augmentation and a regression-based,
rotation-invariant pseudo-label refinement technique. Our pseudo-label
generation method provides multiple geometrically transformed images as inputs
to a convolutional neural network (CNN), regresses the augmented detections
generated by the network to reduce localization errors, and then clusters them
using the mean-shift algorithm. The self-supervised detector model is used in a
single-camera tracking algorithm to generate temporal identifiers for the
targets. Our method also incorporates a multiview trajectory association
mechanism to maintain consistent temporal identifiers as passengers travel
across camera views. 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. Our results show that self-supervision improves object detection
accuracy by up to 42% without increasing the inference time of the model. Our
multicamera association method achieves up to 89% multiobject tracking accuracy
with an average computation time of less than 15 ms.
