The KITTI Vision Benchmark Suite

State-of-the-art generalizable multi-object tracking as edge classification on a continuously evolved temporal multiplex graph, which contains only pairwise geometric relationships between objects (temporal and spatial) as its initial edge features.

We encode 3D detections as nodes in a graph, where spatial and temporal pairwise relations among objects are encoded via localized polar coordinates on graph edges. This representation makes our geometric relations invariant to global transformations and smooth trajectory changes, especially under non-holonomic motion. This allows our graph neural network to learn to effectively encode temporal and spatial interactions and fully leverage contextual and motion cues to obtain final scene interpretation by posing data association as edge classification. We establish a new state-of-the-art on nuScenes and show that PolarMOT generalizes remarkably well across different locations (Boston, Singapore) and datasets (nuScenes and KITTI).

See paper

@inproceedings{polarmot,
author = {Aleksandr Kim and Guillem Bras{'o} and Aljo\v{s}a O\v{s}ep and Laura Leal-Taix{'e}},
title = {PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking?},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2022},
}