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IOU Tracker

Tracking-by-detection is a common approach to multi-object tracking. With ever increasing performances of object detectors, the basis for a tracker becomes much more reliable. In combination with commonly higher frame rates, this poses a shift in the challenges for a successful tracker. We propose a very simple tracking algorithm which can compete with more sophisticated approaches at a fraction of the computational cost. With thorough experiments we show its potential using a wide range of object detectors. The proposed method can easily run at thousands of frames per second (fps) while outperforming the state-of-the-art on the DETRAC vehicle tracking dataset and achieves competitive results on the MOT17 benchmark.

 Publication; Bochinski, E., Eiselein, V., and Sikora, T. High-speed tracking-by-detection without using image information.  AVSS, 2017

Award: Challenge Winner IWOT4S @ AVSS 2017

Link: https://iwt4s.wordpress.com/

Code: https://github.com/bochinski/iou-tracker





V-IOU Tracker

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.

Publication: Bochinski, E., Senst, T. and Sikora, T. Extending IOU based multi-object tracking by visual information. AVSS, 2018


Challenge Winner IWOT4S @ AVSS 2018

We won the VisDrone 2018 Challenge @ ECCV








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