Jiang, K.; Li, Y.; Ma, T.; Li, L. Hierarchical Network-Based Tracklets Data Association for Multiple Extended Target Tracking with Intermittent Measurements. Sensors2023, 23, 6372.
Jiang, K.; Li, Y.; Ma, T.; Li, L. Hierarchical Network-Based Tracklets Data Association for Multiple Extended Target Tracking with Intermittent Measurements. Sensors 2023, 23, 6372.
Jiang, K.; Li, Y.; Ma, T.; Li, L. Hierarchical Network-Based Tracklets Data Association for Multiple Extended Target Tracking with Intermittent Measurements. Sensors2023, 23, 6372.
Jiang, K.; Li, Y.; Ma, T.; Li, L. Hierarchical Network-Based Tracklets Data Association for Multiple Extended Target Tracking with Intermittent Measurements. Sensors 2023, 23, 6372.
Abstract
The main problem in pursuing multiple extended targets tracking is distinguishing the origins of the measurements. The association of measurements to the possible origins within the target’s extent is difficult, especially for the occlusions or the detection blind zone which cause the intermittent measurements. To solve the problem, a hierarchical network-based tracklets data association algorithm is proposed. At the low level, the min cost network flow model is used to extract possible tracklets from the divided measurement set. At the high-level, the trajectories are estimated from the tracks produced by the previous low level network. The experimental results show that the hierarchical network-based tracklets data association algorithm outperforms the JPDA and RFS-based method when the measurement is intermittently unavailable.
Computer Science and Mathematics, Information Systems
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