Supervised and Unsupervised Detections for Multiple Object Tracking in Traffic Scenes: A Comparative Study
This work addresses tracking road users in traffic scenes, offering insights into detection input choices, but it is incremental as it builds on existing tracking frameworks.
The paper tackles multiple object tracking in traffic scenes by proposing MF-Tracker, which integrates classical and modern features, and investigates the impact of supervised vs. unsupervised detection inputs, achieving MOTA scores ranging from 0.3491 to 0.7638 across datasets.
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.