Volume 23 Issue 6
Dec.  2023
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2023  >  23(6): 283-300
HUANG He, LI Wen-long, YANG Lan, WANG Hui-feng, RU Feng, GAO Tao. Vehicle long-term target tracker optimized by improved carnivorous plant algorithm[J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 283-300. doi: 10.19818/j.cnki.1671-1637.2023.06.019
Citation: HUANG He, LI Wen-long, YANG Lan, WANG Hui-feng, RU Feng, GAO Tao. Vehicle long-term target tracker optimized by improved carnivorous plant algorithm[J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 283-300. doi: 10.19818/j.cnki.1671-1637.2023.06.019
shu

Vehicle long-term target tracker optimized by improved carnivorous plant algorithm

doi: 10.19818/j.cnki.1671-1637.2023.06.019
  • 1.

    Xi'an Key Laboratory of Intelligent Expressway Information Fusion and Control, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    School of Electronics and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 3.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 4.

    School of Energy and Electrical Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

Funds:

National Key Research and Development Program of China 2021YFB2501200

National Natural Science Foundation of China 52172379

National Natural Science Foundation of China 52172324

Key Research and Development Program of Shaanxi Province 2021SF-483

Fundamental Research Funds for the Central Universities 300102323501

Fundamental Research Funds for the Central Universities 300102323502

More Information
  • Author Bio:

    HUANG He(1979-), male, professor, PhD, huanghe@chd.edu.cn

  • Received Date: 2023-05-21
  • Publish Date: 2023-12-25
    Abstract
  • The mechanism of the swarm intelligence (SI) algorithm related to target tracking was studied. The fast histogram of oriented gradients (FHOG) of the tracking region was extracted as a feature template, and a carnivorous plant algorithm (CPA) was employed to search for the target's position in the image search region. A tracking framework based on the CPA was designed using the Bhattacharyya distance as the similarity function for template matching. Considering the complexity situations in the actual tracking process, such as the occlusion and busy background, a short-term memory module was designed to predict the individual initialized by the CPA during the tracking. This module, utilizing a Gaussian distribution, predicted the motion trajectory according to the target's position in the first two frames of the video sequence. To better optimize the target tracking with the CPA, a random tracking strategy and a population division mechanism were developed in the iterative process and integrated into the tracking framework as a search strategy. To make up for the poor representation ability of the single feature of the target by the FHOG, Conv2-1 and Conv4-1 features of ResNet-50 were integrated on the basis of the FHOG. A dynamic update template of the adaptive learning rate was designed based on this fused feature. A two-dimensional scale perception factor was added to the population dimension, allowing the aspect ratio of the target window to vary, so as to better adapt to the change in the scale of the target window. Analysis results show that the introduction of the random tracking strategy and population division mechanism significantly improves the iteration speed and optimization capability of the CPA. The fused feature and adaptive template update enhance the representation of target features, addressing the issues related to learning abundant irrelevant information due to the occlusion and preventing feature template degradation. The proposed algorithm demonstrates notable performance in tracking challenging vehicle video sequences from UAV123. The precision and success rate are 0.81 and 0.58, respectively, with a speed of 11.05 frames per second. Compared with the algorithms in similar literature, the tracking accuracy and robustness of the proposed algorithm improves substantially, making it adaptable to environmental change in complex scenes and ensuring a stable long-term tracking of target vehicles.

     

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