arXiv subjects

Maria Damanaki

Publications and source records attributed to Maria Damanaki.

3 recordsLinked to original sources

arXiv2026

CDKF-Track: Cluster-aware Data-Driven Kalman Filtering for Cooperative 3D Multi-Object Tracking

Multi-Object Tracking (MOT) is essential for EdgeAI perception systems, where accurate object localization and reliable identification enable safe decision-making. Singleagent MOT suffers from occlusions, sensor noise, and partial scene understanding in complex real-world scenarios. While multi-agent systems improve robustness by exploiting shared information, they introduce redundant measurements that lead to false data associations, and still struggle to capture nonlinear object dynamics. To address these challenges, we propose CDKFTrack, a Cluster-aware Data-Driven Kalman Filtering framework for Cooperative 3D MOT. The proposed method first fuses multivehicle 3D LiDAR detections through a Graph Laplacian-based formulation. Then, a cluster-aware redundancy reduction scheme groups spatially related detections and selects representative observations to reduce duplicate inputs to the tracker. The resulting detections are processed by a data-driven Kalman filter that learns object motion dynamics from data, reducing dependence on predefined linear motion assumptions. Furthermore, a wavelet-based temporal refinement module leverages the multiresolution decomposition property of wavelets to attenuate shortterm positional fluctuations and improve trajectory continuity. To the best of our knowledge, CDKF-Track is the first framework to jointly address detection-level fusion redundancy and learnable motion modeling in cooperative 3D MOT. Experimental results on the real-world V2V4Real dataset indicate that CDKF-Track achieves up to 27.99% improvements in tracking accuracy over state-of-the-art multi-agent MOT methods.

cs.RO↗

arXiv2025

Robustifying 3D Perception via Least-Squares Graphs for Multi-Agent Object Tracking

The critical perception capabilities of EdgeAI systems, such as autonomous vehicles, are required to be resilient against adversarial threats, by enabling accurate identification and localization of multiple objects in the scene over time, mitigating their impact. Single-agent tracking offers resilience to adversarial attacks but lacks situational awareness, underscoring the need for multi-agent cooperation to enhance context understanding and robustness. This paper proposes a novel mitigation framework on 3D LiDAR scene against adversarial noise by tracking objects based on least-squares graph on multi-agent adversarial bounding boxes. Specifically, we employ the least-squares graph tool to reduce the induced positional error of each detection's centroid utilizing overlapped bounding boxes on a fully connected graph via differential coordinates and anchor points. Hence, the multi-vehicle detections are fused and refined mitigating the adversarial impact, and associated with existing tracks in two stages performing tracking to further suppress the adversarial threat. An extensive evaluation study on the real-world V2V4Real dataset demonstrates that the proposed method significantly outperforms both state-of-the-art single and multi-agent tracking frameworks by up to 23.3% under challenging adversarial conditions, operating as a resilient approach without relying on additional defense mechanisms.

cs.CV↗

arXiv2025

Optimizing Cooperative Multi-Object Tracking using Graph Signal Processing

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.

cs.CV↗