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3D-DIoU: 3D Distance Intersection over Union for Multi-Object Tracking in Point Cloud.

Sazan Ali Kamal Mohammed1,2, Mohd Zulhakimi Ab Razak1, Abdul Hadi Abd Rahman3

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Summary
This summary is machine-generated.

This study introduces a novel multi-object tracking (MOT) method using 3D distance IoU non-maximum suppression (DIoU-NMS) and 3D-DIoU for enhanced object association. The approach significantly improves tracking accuracy and speed in complex point cloud scenes.

Keywords:
3D-DIoUDIoU-NMSmotion predictionmulti-object trackingmultistage data associationpoint cloudtracklets

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Area of Science:

  • Computer Vision
  • Point Cloud Processing
  • Multi-Object Tracking (MOT)

Background:

  • Multi-object tracking (MOT) in point clouds faces challenges with occlusion and similar objects, degrading performance.
  • Current 'tracking-by-detection' methods suffer from increased ID switches and tracking drifts in complex scenes.
  • Predicting object locations accurately in occluded or complex scenarios remains difficult.

Purpose of the Study:

  • To enhance the robustness and speed of 3D multi-object tracking.
  • To address performance degradation caused by occlusion and object similarity in point cloud data.
  • To improve the accuracy of object detection and association in challenging tracking scenarios.

Main Methods:

  • Employed a novel 3D distance IoU non-maximum suppression (DIoU-NMS) for accurate 3D object detection.
  • Utilized 3D-DIoU for robust object association, improving tracking reliability.
  • Developed a hybrid approach combining 3D DIoU-NMS and 3D-DIoU for efficient tracking.

Main Results:

  • Significant improvement in tracking speed was achieved through the hybrid 3D DIoU-NMS and 3D-DIoU method.
  • Experimental results on Waymo Open Dataset and nuScenes dataset show clear benefits over existing algorithms.
  • The proposed multistage data association and tracking technique demonstrated superior tracking accuracy.

Conclusions:

  • The developed 3D MOT method significantly enhances tracking accuracy and speed.
  • The hybrid DIoU-NMS and 3D-DIoU approach effectively tackles challenges like occlusion and object similarity.
  • This technique offers a substantial advancement compared to current 3D MOT methods.