Neural Enhanced Belief Propagation for Data Assocation in Multiobject Tracking

• URL: http://arxiv.org/abs/2203.09948v1
• Date: Thu, 17 Mar 2022 00:12:48 GMT
• Title: Neural Enhanced Belief Propagation for Data Assocation in Multiobject Tracking
• Authors: Mingchao Liang and Florian Meyer
• Abstract summary: Multiobject tracking (MOT) will create new services and applications in fields such as autonomous navigation and applied ocean sciences. Belief propagation (BP) is a state-of-the-art method for Bayesian MOT but fully relies on a statistical model and preprocessed sensor measurements. We establish a hybrid method for model-based and data-driven MOT. The proposed neural enhanced belief propagation (NEBP) approach complements BP by information learned from raw sensor data. We evaluate the performance of our NEBP approach for MOT on the nuScenes autonomous driving dataset and demonstrate that it can outperform state-of-
• Score: 8.228150100178983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Situation-aware technologies enabled by multiobject tracking (MOT) methods will create new services and applications in fields such as autonomous navigation and applied ocean sciences. Belief propagation (BP) is a state-of-the-art method for Bayesian MOT but fully relies on a statistical model and preprocessed sensor measurements. In this paper, we establish a hybrid method for model-based and data-driven MOT. The proposed neural enhanced belief propagation (NEBP) approach complements BP by information learned from raw sensor data with the goal to improve data association and to reject false alarm measurements. We evaluate the performance of our NEBP approach for MOT on the nuScenes autonomous driving dataset and demonstrate that it can outperform state-of-the-art reference methods.

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