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Distributed Multi-Object Tracking Under Limited Field of View Sensors.

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Distributed Multi-Object Tracking Under Limited Field of View Sensors

This repository is provided as part of the our TSP paper:

H. V. Nguyen, H. Rezatofighi, B.-N. Vo, D. Ranasinghe, "Distributed Multi-Object Tracking Under Limited Field of View Sensors". IEEE Transactions on Signal Processing, vol. 69, pp. 5329-5344, 2021, doi: 10.1109/TSP.2021.3103125. Paper.

If you use our provided codes, please consider cite our paper using:

@article{nguyen2021distributed,
	title={Distributed Multi-Object Tracking Under Limited Field of View Sensors},
	author={Nguyen, Hoa Van and Rezatofighi, Hamid and Vo, Ba-Ngu and Ranasinghe, Damith C},
	journal = {{IEEE Transactions on Signal Processing}},
	year={2021
	volume={69},
	number={},
	pages={5329-5344},
	doi={10.1109/TSP.2021.3103125}
}

Brief information

  1. All objects move follow a 4D Constant Velocity (x/y position and velocity) with 2D observations (position only).

  2. Each node runs an LMB filter with joint prediction and update step, with a measurement-based Adaptive Birth Procedure (from data of previous measurements).

  3. Call demo.mto run it, with the following settings for different scenarios in the Paper:

    • Scenario 1 by a pair of (property, value) of 'case_id',1 in gen_settings:

      settings =  gen_settings('case_id',1,'sel_pd',0.98);  
    • Scenario 2 by a pair of (property, value) of 'case_id',2 in gen_settings:

      settings =  gen_settings('case_id',2,'sel_pd',0.98);  
      
    • Scenario 3 by a pair of (property, value) of 'case_id',3 in gen_settings:

      settings =  gen_settings('case_id',3,'sel_pd',0.98);  
      
  4. We only publish our proposed Track Consensus method:

    • Track consensus using OSPA2TC-OSPA2 by setting a pair of (property, value) to 'metric_type','ospa_union' in gen_model:

      model = gen_model(settings,'meas_sigma',10,'lambda_c',10,'track_threshold',0.001,'metric_type','ospa_union'); 
    • Track consensus using Wasserstein TC-WASS by setting a pair of (property, value) to 'metric_type','wasserstein':

      model = gen_model(settings,'meas_sigma',10,'lambda_c',10,'track_threshold',0.001,'metric_type','wasserstein');  
  5. Codes are provided for academic research purposes only. For a commercial license, please contact me at hoavan.nguyen@adelaide.edu.au.

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