Fisheye Multiple Object Tracking by Learning Distortions without Dewarping

We develop a new Multiple Object Tracking (MOT) scheme for fisheye cameras that can directly perform vehicle detection, re-identification, and tracking under fisheye distortions without explicit dewarping. Fisheye cameras provide omnidirectional coverage that is wider than traditional cameras, reducing fewer need of cameras to monitor road intersections. However, the problem of distorted views introduces new challenges for fisheye MOT. In this paper, we propose a Fish-Eye Multiple Object Tracking (FEMOT) approach with two novelties. We develop the Distorted Fisheye Image Augmentation (DFIA) method to improve object detection and re-identification on fisheye cameras, where fisheye model training can be performed on existing datasets of traditional cameras via fisheye data synthesis and augmentation. We also develop the Hybrid Data Association (HDA) method to perform tracking directly on fisheye views, without the need of dewarping. The developed FEMOT framework provides practical design and advancement that enables large-scale use of fisheye cameras in smart city and surveillance applications.