TY - GEN
T1 - A Novel Multiple Hypothesis Tracking Algorithm Integrated with Detection Processing
AU - Wang, Ziwei
AU - Sun, Jinping
AU - Wang, Naiyu
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - In current multi-targets tracking system, the multiple hypotheses tracking (MHT) algorithm is considered as the preferred algorithm for data association. However, this method is straightforward, and ignores the couple relationship between detector and tracker, which may result system performance loss. In this paper, we propose a novel multiple hypothesis tracking algorithm integrated with detection processing (MHT-IDP) for multi-target tracking. It explores the idea of integrating the detector with the tracker, in which the tracker guides the detector where to search a target, and the detector provides what is discovered. Moreover, we find that the Bayesian detection threshold is lower near the predicted measurement. Simulation results demonstrate that the MHT-IDP algorithm can improve the clutter suppression effect, enhance the detection performance of the target, and achieve better performance in tracking accuracy compared with the MHT algorithm.
AB - In current multi-targets tracking system, the multiple hypotheses tracking (MHT) algorithm is considered as the preferred algorithm for data association. However, this method is straightforward, and ignores the couple relationship between detector and tracker, which may result system performance loss. In this paper, we propose a novel multiple hypothesis tracking algorithm integrated with detection processing (MHT-IDP) for multi-target tracking. It explores the idea of integrating the detector with the tracker, in which the tracker guides the detector where to search a target, and the detector provides what is discovered. Moreover, we find that the Bayesian detection threshold is lower near the predicted measurement. Simulation results demonstrate that the MHT-IDP algorithm can improve the clutter suppression effect, enhance the detection performance of the target, and achieve better performance in tracking accuracy compared with the MHT algorithm.
KW - Bayesian detection threshold
KW - multi- target tracking
KW - multiple hypothesis track algorithm
KW - the predicted measurement
UR - https://www.scopus.com/pages/publications/85091952319
U2 - 10.1109/ICSIDP47821.2019.9173205
DO - 10.1109/ICSIDP47821.2019.9173205
M3 - 会议稿件
AN - SCOPUS:85091952319
T3 - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
BT - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019
Y2 - 11 December 2019 through 13 December 2019
ER -