Diffusion Models for Multi-target Adversarial Tracking
2023 INTERNATIONAL SYMPOSIUM ON MULTI-ROBOT AND MULTI-AGENT SYSTEMS, MRS(2023)
Abstract
Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction, where the knowledge of an adversarial target's location is often limited. Improving autonomous tracking systems will enable unmanned aerial, surface, and underwater vehicles to better assist in interdicting smugglers that use manned surface, semisubmersible, and aerial vessels. As unmanned drones proliferate, accurate autonomous target estimation is even more crucial for security and safety. This paper presents Constrained Agent-based Diffusion for ENhanCEd Multi-Agent Tracking (CADENCE), an approach aimed at generating comprehensive predictions of adversary locations by leveraging past sparse state information. To assess the effectiveness of this approach, we evaluate predictions on single-target and multi-target pursuit environments, employing Monte-Carlo sampling of the diffusion model to estimate the probability associated with each generated trajectory. We propose a novel cross-attention based diffusion model that utilizes constraint-based sampling to generate multimodal track hypotheses. Our single-target model surpasses the performance of all baseline methods on Average Displacement Error (ADE) for predictions across all time horizons.
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Key words
Diffusion Model,Track Model,Multi-target Tracking,Unmanned Aerial Vehicles,Drug Trafficking,Target Tracking,Autonomous Surface Vehicles,Bimodal,Computer Vision,Mixture Model,Kalman Filter,Inequality Constraints,Environmental Constraints,Gaussian Mixture Model,Motion Model,Particle Filter,Prediction Horizon,Environmental Agents,Targeting Agents,Single Tracking,Large-scale Environments,Partial Observation,Model-free Approach,Trajectory Generation,Computer Vision Community,Sparse Observations,Actual Trajectory
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