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Quantifying Path Smoothness in Video Object Tracking by Detection

Published onJun 05, 2023
Quantifying Path Smoothness in Video Object Tracking by Detection


Object detection and tracking are important areas of research in computer vision. Computer vision solutions to object detection are typically single-frame solutions. To perform tracking by detection, these solutions typically do object detection on a perframe basis, thus losing any temporal information from previous frames. Many multiobject tracking solutions report the average precision performance on video datasets, but they do not evaluate the temporal qualities of these solutions. In video, not only the detection of objects is important but the temporal motion attributes of an object’s path, such as its velocity, acceleration, and jerk, are important as well. Many implementations of Object Tracking by Detection systems have run into the problem of motion smoothing for bounding box paths. This paper focuses on quantifying the smoothness of detected object paths within some temporal window. We propose using two smoothness metrics from the field of biokinematics and adapt them for use with detections. Finally, using these metrics, we evaluate the ground truth and two popular object detectors, at the time of experimentation (YOLOv3 and Retinanet), on the entire MOT17 dataset. The results show that the metrics are useful in determining object smoothness, and provide us with an additional approach to evaluate an algorithm’s performance in object tracking. The experiments also demonstrate that YOLOv3 produces smoother bounding boxes than Retinanet. All supplemental graphs and data are shown in our appendix

Article ID: 2023L11

Month: June

Year: 2023

Address: Online

Venue: The 36th Canadian Conference on Artificial Intelligence

Publisher: Canadian Artificial Intelligence Association


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