AutoSelect: Automatic and Dynamic Detection Selection for 3D Multi-Object Tracking

TL;DR

AutoSelect introduces an automatic, dynamic detection filtering method for 3D multi-object tracking, reducing manual tuning and improving accuracy by adaptively selecting high-quality detections per frame or object.

Contribution

The paper proposes a novel automatic detection selection approach that dynamically adjusts thresholds for each frame or object, eliminating manual threshold tuning in 3D multi-object tracking.

Findings

Filters out 45.7% false positives while maintaining recall

Achieves state-of-the-art performance on KITTI and nuScenes datasets

Removes need for manual threshold search

Abstract

3D multi-object tracking is an important component in robotic perception systems such as self-driving vehicles. Recent work follows a tracking-by-detection pipeline, which aims to match past tracklets with detections in the current frame. To avoid matching with false positive detections, prior work filters out detections with low confidence scores via a threshold. However, finding a proper threshold is non-trivial, which requires extensive manual search via ablation study. Also, this threshold is sensitive to many factors such as target object category so we need to re-search the threshold if these factors change. To ease this process, we propose to automatically select high-quality detections and remove the efforts needed for manual threshold search. Also, prior work often uses a single threshold per data sequence, which is sub-optimal in particular frames or for certain objects. Instead, we dynamically search threshold per frame or per object to further boost performance. Through experiments on KITTI and nuScenes, our method can filter out $45.7\%$ false positives while maintaining the recall, achieving new S.O.T.A. performance and removing the need for manually threshold tuning.