|Table of Contents|

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

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2023年06期
Page:
283-300
Research Field:
交通信息工程及控制
Publishing date:
2023-12-30

Info

Title:
Vehicle long-term target tracker optimized by improved carnivorous plant algorithm
Author(s):
HUANG He1; 2;  LI Wen-long1; 2;  YANG Lan3;  WANG Hui-feng2;  RU Feng1; 4;  GAO Tao3
(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)
Keywords:
intelligent transportation;  target tracking;  long-term vehicle tracking;  swarm intelligence algorithm;  carnivorous plant algorithm;  feature extraction;  similarity function;  scale perception
PACS:
U491.1
DOI:
10.19818/j.cnki.1671-1637.2023.06.019
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 CPAduring 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. Comparedwith 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. 1 tab, 23 figs, 31 refs.

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Last Update: 2023-12-30