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Cooperative multi-sensor tracking of vulnerable road users in the presence of missing detections

Martin Dimitrievski (UGent) , David Van Hamme (UGent) , Peter Veelaert (UGent) and Wilfried Philips (UGent)
(2020) SENSORS. 20(17).
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Abstract
This paper presents a vulnerable road user (VRU) tracking algorithm capable of handling noisy and missing detections from heterogeneous sensors. We propose a cooperative fusion algorithm for matching and reinforcing of radar and camera detections using their proximity and positional uncertainty. The belief in the existence and position of objects is then maximized by temporal integration of fused detections by a multi-object tracker. By switching between observation models, the tracker adapts to the detection noise characteristics making it robust to individual sensor failures. The main novelty of this paper is an improved imputation sampling function for updating the state when detections are missing. The proposed function uses a likelihood without association that is conditioned on the sensor information instead of the sensor model. The benefits of the proposed solution are two-fold: firstly, particle updates become computationally tractable and secondly, the problem of imputing samples from a state which is predicted without an associated detection is bypassed. Experimental evaluation shows a significant improvement in both detection and tracking performance over multiple control algorithms. In low light situations, the cooperative fusion outperforms intermediate fusion by as much as 30%, while increases in tracking performance are most significant in complex traffic scenes.
Keywords
multi-object tracking, switching observation model, multiple imputations, particle filter, cooperative sensor fusion, people tracking, radar, camera, FUSION, IMPUTATIONS

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MLA
Dimitrievski, Martin, et al. “Cooperative Multi-Sensor Tracking of Vulnerable Road Users in the Presence of Missing Detections.” SENSORS, vol. 20, no. 17, 2020, doi:10.3390/s20174817.
APA
Dimitrievski, M., Van Hamme, D., Veelaert, P., & Philips, W. (2020). Cooperative multi-sensor tracking of vulnerable road users in the presence of missing detections. SENSORS, 20(17). https://doi.org/10.3390/s20174817
Chicago author-date
Dimitrievski, Martin, David Van Hamme, Peter Veelaert, and Wilfried Philips. 2020. “Cooperative Multi-Sensor Tracking of Vulnerable Road Users in the Presence of Missing Detections.” SENSORS 20 (17). https://doi.org/10.3390/s20174817.
Chicago author-date (all authors)
Dimitrievski, Martin, David Van Hamme, Peter Veelaert, and Wilfried Philips. 2020. “Cooperative Multi-Sensor Tracking of Vulnerable Road Users in the Presence of Missing Detections.” SENSORS 20 (17). doi:10.3390/s20174817.
Vancouver
1.
Dimitrievski M, Van Hamme D, Veelaert P, Philips W. Cooperative multi-sensor tracking of vulnerable road users in the presence of missing detections. SENSORS. 2020;20(17).
IEEE
[1]
M. Dimitrievski, D. Van Hamme, P. Veelaert, and W. Philips, “Cooperative multi-sensor tracking of vulnerable road users in the presence of missing detections,” SENSORS, vol. 20, no. 17, 2020.
@article{8672556,
  abstract     = {This paper presents a vulnerable road user (VRU) tracking algorithm capable of handling noisy and missing detections from heterogeneous sensors. We propose a cooperative fusion algorithm for matching and reinforcing of radar and camera detections using their proximity and positional uncertainty. The belief in the existence and position of objects is then maximized by temporal integration of fused detections by a multi-object tracker. By switching between observation models, the tracker adapts to the detection noise characteristics making it robust to individual sensor failures. The main novelty of this paper is an improved imputation sampling function for updating the state when detections are missing. The proposed function uses a likelihood without association that is conditioned on the sensor information instead of the sensor model. The benefits of the proposed solution are two-fold: firstly, particle updates become computationally tractable and secondly, the problem of imputing samples from a state which is predicted without an associated detection is bypassed. Experimental evaluation shows a significant improvement in both detection and tracking performance over multiple control algorithms. In low light situations, the cooperative fusion outperforms intermediate fusion by as much as 30%, while increases in tracking performance are most significant in complex traffic scenes.},
  articleno    = {4817},
  author       = {Dimitrievski, Martin and Van Hamme, David and Veelaert, Peter and Philips, Wilfried},
  issn         = {1424-8220},
  journal      = {SENSORS},
  keywords     = {multi-object tracking,switching observation model,multiple imputations,particle filter,cooperative sensor fusion,people tracking,radar,camera,FUSION,IMPUTATIONS},
  language     = {eng},
  number       = {17},
  pages        = {40},
  title        = {Cooperative multi-sensor tracking of vulnerable road users in the presence of missing detections},
  url          = {http://dx.doi.org/10.3390/s20174817},
  volume       = {20},
  year         = {2020},
}

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