Multi-object tracking (MOT) has been notoriously difficult to evaluate. Previous metrics
overemphasize the importance of either detection or association. To address this, we …

Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This
representation mimics the well-studied image-based 2D bounding-box detection but comes …

Tracking has traditionally been the art of following interest points through space and time.
This changed with the rise of powerful deep networks. Nowadays, tracking is dominated by …

Tracking by detection, the dominant approach for online multi-object tracking, alternates
between localization and association steps. As a result, it strongly depends on the quality of …

Standardized benchmarks have been crucial in pushing the performance of computer vision
algorithms, especially since the advent of deep learning. Although leaderboards should not …

Object detection and data association are critical components in multi-object tracking (MOT)
systems. Despite the fact that the two components are dependent on each other, prior works …

3D multi-object tracking (MOT) is an essential component for many applications such as
autonomous driving and assistive robotics. Recent work on 3D MOT focuses on developing …

Multi-object tracking (MOT) enables mobile robots to perform well-informed motion planning
and navigation by localizing surrounding objects in 3D space and time. Existing methods …

In a point cloud sequence, 3D object tracking aims to predict the location and orientation of
an object in the current search point cloud given a template point cloud. Motivated by the …

Abstract 3D Multi-object tracking (MOT) is crucial to autonomous systems. Recent work uses
a standard tracking-by-detection pipeline, where feature extraction is first performed …