Extending IOU Based Multi-Object Tracking by Visual Information

Today's multi-object tracking approaches benefit greatly from nearly perfect object detections when following the popular tracking-by-detection scheme. This allows for extremely simple but accurate tracking methods which completely rely on the input detections as the high-speed IOU tracker. For real world applications, few missing detections cause a high number of ID switches and fragmentations which degrades the quality of the tracks significantly. We show that this problem can be efficiently overcome if the tracker falls back to visual single-object tracking in cases where no object detection is available. In several experiments we show for different visual trackers that the number of ID switches and fragmentations can be reduced by a large amount while maintaining high tracking speeds and outperforming the state-of-the art for the UA-DETRAC and VisDrone datasets.