Negative Information for Occlusion Reasoning in Dynamic Extended Multiobject Tracking

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Abstract
A novel approach to utilize negative information to improve the precision and accuracy of extended multiobject tracking is presented. The parameterized probability density of object tracks undetected in sensor data is updated via inferences about the conditions necessary to result in occlusion of the undetected object. Negative information is also leveraged to inform track existence and data association, both of which contribute to a more sensible belief of the local dynamic scene. Simulation and experimental results are presented from autonomous driving scenarios, demonstrating that the use of negative information leads to a more complete, accurate, precise, and intuitive belief of the local scene, enabling high-level tasks that would otherwise be impractical.
Funding Information
  • NSF, CPS (CNS-0931686)
  • ARO (W911NF-09-1-0466)

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