TY  - JOUR
AU  - Shen, Jiashi
AU  - Yang, Chaoqun
AU  - He, Lidong
AU  - Cao, Xianghui
PY  - 2025
DA  - 2025/06/25
TI  - A Track Splitting Determination Method for Elliptical Extended Targets Based on Spatio Temporal Similarity
JO  - Chinese Journal of Information Fusion
T2  - Chinese Journal of Information Fusion
JF  - Chinese Journal of Information Fusion
VL  - 2
IS  - 2
SP  - 171
EP  - 181
DO  - 10.62762/CJIF.2025.519610
UR  - https://www.icck.org/article/abs/CJIF.2025.519610
KW  - extended target tracking
KW  - target splitting
KW  - gaussian wasserstein distance
KW  - spatiotemporal trajectories
KW  - error correction
AB  - Extended target tracking in occlusion scenarios often suffers from split errors due to sensor limitations and complex target interactions, leading to degraded tracking performance for autonomous vehicles and surveillance systems. To address this issue, in this paper, we propose a Gaussian Wasserstein distance-enhanced spatio-temporal similarity method for split error correction. We first analyze the spatio-temporal characteristics of split extended targets and model their geometric uncertainties via elliptical Gaussian distributions. Then, we integrate the Gaussian Wasserstein distance into the clue-aware trajectory similarity calculation framework to simultaneously capture positional and shape discrepancies, and designs an adaptive validation gate mechanism to dynamically adjust the threshold for track splitting, enabling accurate determination and fusion of split targets. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed method.
SN  - 2998-3371
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Shen2025A,
  author = {Jiashi Shen and Chaoqun Yang and Lidong He and Xianghui Cao},
  title = {A Track Splitting Determination Method for Elliptical Extended Targets Based on Spatio Temporal Similarity},
  journal = {Chinese Journal of Information Fusion},
  year = {2025},
  volume = {2},
  number = {2},
  pages = {171-181},
  doi = {10.62762/CJIF.2025.519610},
  url = {https://www.icck.org/article/abs/CJIF.2025.519610},
  abstract = {Extended target tracking in occlusion scenarios often suffers from split errors due to sensor limitations and complex target interactions, leading to degraded tracking performance for autonomous vehicles and surveillance systems. To address this issue, in this paper, we propose a Gaussian Wasserstein distance-enhanced spatio-temporal similarity method for split error correction. We first analyze the spatio-temporal characteristics of split extended targets and model their geometric uncertainties via elliptical Gaussian distributions. Then, we integrate the Gaussian Wasserstein distance into the clue-aware trajectory similarity calculation framework to simultaneously capture positional and shape discrepancies, and designs an adaptive validation gate mechanism to dynamically adjust the threshold for track splitting, enabling accurate determination and fusion of split targets. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed method.},
  keywords = {extended target tracking, target splitting, gaussian wasserstein distance, spatiotemporal trajectories, error correction},
  issn = {2998-3371},
  publisher = {Institute of Central Computation and Knowledge}
}