[1]CHEN Lichao,XIE Dan,CAO Jianfang,et al.Research on vehicle real-time detection algorithm based on improved optical flow method and GMM[J].CAAI Transactions on Intelligent Systems,2021,16(2):271-278.[doi:10.11992/tis.201907051]
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Research on vehicle real-time detection algorithm based on improved optical flow method and GMM

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 2 Page number: 271-278 Column: 学术论文—机器感知与模式识别 Public date: 2021-03-05
Title:
Research on vehicle real-time detection algorithm based on improved optical flow method and GMM
Author(s):
CHEN Lichao1;  XIE Dan1;  CAO Jianfang1;  ZHANG Rui1
1. School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China;
2. Department of Computer Science and Technology, Xinzhou Teachers University, Xinzhou 034000, China
Keywords:
IOFGMM detection algorithm; optical flow method; gaussian mixture background model; Information fusion; real-time detection; gradient; illumination; constraint
CLC:
TP391
DOI:
10.11992/tis.201907051
Abstract:
To solve the problem of the optical flow algorithm being greatly affected by illumination and the highly variable detection effect in different scenes, in this paper, we propose an improved optical flow and Gaussian mixture model (IOFGMM) algorithm for the real-time detection of moving vehicles. First, a restriction is added to the optical flow algorithm whereby different constraints are used at different points. Then, a Gaussian mixture model (GMM) is fused. Finally, the number of target boxes and the area in which the target boxes overlap are compared by the proposed fusion algorithm. The vehicle detection information after fusion is displayed in the surveillance video. Experimental results show that the detection performance of the IOFGMM algorithm achieved an average accuracy rate of 84.80%, an average recall rate of 84.79%, and an average F1 value of 84.63% for videos of three different scenes. Compared with the classical optical flow method, the GMM, and the algorithm based on these two theories, the IOFGMM algorithm shows an average improvement of 37% in each metric. Therefore, we can conclude that the IOFGMM algorithm has good detection performance.
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