Optimizing Cooperative Multi-Object Tracking using Graph Signal Processing

TL;DR

This paper introduces a graph signal processing-based cooperative multi-object tracking framework for 3D LiDAR data, improving tracking accuracy by fusing multi-vehicle detections and reducing errors.

Contribution

It presents a novel graph topology-aware optimization method for multi-agent object tracking, leveraging Graph Laplacian processing to enhance localization and tracking accuracy.

Findings

Significantly outperforms baseline methods on V2V4Real dataset

Effectively fuses multi-vehicle detections for improved accuracy

Reduces bounding box localization errors

Abstract

Multi-Object Tracking (MOT) plays a crucial role in autonomous driving systems, as it lays the foundations for advanced perception and precise path planning modules. Nonetheless, single agent based MOT lacks in sensing surroundings due to occlusions, sensors failures, etc. Hence, the integration of multiagent information is essential for comprehensive understanding of the environment. This paper proposes a novel Cooperative MOT framework for tracking objects in 3D LiDAR scene by formulating and solving a graph topology-aware optimization problem so as to fuse information coming from multiple vehicles. By exploiting a fully connected graph topology defined by the detected bounding boxes, we employ the Graph Laplacian processing optimization technique to smooth the position error of bounding boxes and effectively combine them. In that manner, we reveal and leverage inherent coherences of diverse multi-agent detections, and associate the refined bounding boxes to tracked objects at two stages, optimizing localization and tracking accuracies. An extensive evaluation study has been conducted, using the real-world V2V4Real dataset, where the proposed method significantly outperforms the baseline frameworks, including the state-of-the-art deep-learning DMSTrack and V2V4Real, in various testing sequences.