object tracking based on millimeter wave radar data

by dawnlh

Object tracking based on millimeter wave radar data with Kalman Filter algorithm.

• In multiple object tracking, when objects have overlapping, mistakes may occur. This problem can perhaps be solved by using a more robust detectionToTrackAssignment.m function. Specifically, we can take other statistic features into account when calculating the cost in detectionToTrackAssignment.m function. Currently, only position and speed are considerer.
• The performance of the implemented algorithm is very dependent on parameter tuning, especially the parameters of DBSCAN and the tracking module (like parameters in detectionToTrackAssignment.m and updateTrackStates.m).
• Tips to improve the performance
  • A more sophisticated denoising algorithm. Current point_cloud_denoise.m only removes the static points and out-of-range points, the "real" noise is not filtered. A better denoising algorithm may consider the spatial-temporal information between adjacent frames and adjacent regions.
  • A more sophisticated cluster algorithm, which can automatically figure out different objects in tough cases like overlapping.
  • A more sophisticated tracking algorithm and strategy, which take more information into consideration and realize better performance.
  • Optimize the code to lower the computation cost and speed up the running speed.

• P. Zhao et al., “mID: Tracking and Identifying People with Millimeter Wave Radar,” in 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), Santorini Island, Greece, May 2019, pp. 33–40. doi: [10.1109/DCOSS.2019.00028

    ](https://doi.org/10.1109/DCOSS.2019.00028
    
    ).
  

• MATLAB: Motion-Based Multiple Object Tracking

• MATLAB: Use Kalman Filter for Object Tracking