Supervised and Unsupervised Detections for Multiple Object Tracking in Traffic Scenes: A Comparative Study

• URL: http://arxiv.org/abs/2003.13644v1
• Date: Mon, 30 Mar 2020 17:27:04 GMT
• Title: Supervised and Unsupervised Detections for Multiple Object Tracking in Traffic Scenes: A Comparative Study
• Authors: Hui-Lee Ooi, Guillaume-Alexandre Bilodeau, and Nicolas Saunier
• Abstract summary: We propose a multiple object tracker, called MF-Tracker, that integrates multiple classical features (spatial and colours) and modern features (detection labels and re-identification features) in its tracking framework. Since our tracker can work with detections coming either from unsupervised and supervised object detectors, we also investigated the impact of supervised and unsupervised detection inputs in our method. Results show that our proposed method is performing very well in both datasets with different inputs.
• Score: 11.024591739346294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a multiple object tracker, called MF-Tracker, that integrates multiple classical features (spatial distances and colours) and modern features (detection labels and re-identification features) in its tracking framework. Since our tracker can work with detections coming either from unsupervised and supervised object detectors, we also investigated the impact of supervised and unsupervised detection inputs in our method and for tracking road users in general. We also compared our results with existing methods that were applied on the UA-Detrac and the UrbanTracker datasets. Results show that our proposed method is performing very well in both datasets with different inputs (MOTA ranging from 0:3491 to 0:5805 for unsupervised inputs on the UrbanTracker dataset and an average MOTA of 0:7638 for supervised inputs on the UA Detrac dataset) under different circumstances. A well-trained supervised object detector can give better results in challenging scenarios. However, in simpler scenarios, if good training data is not available, unsupervised method can perform well and can be a good alternative.

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