Vehicle type classification using graph ant colony optimizer based stack autoencoder model

被引:1
|
作者
Rani, B. Kavitha [1 ]
Rao, M. Varaprasad [1 ]
Patra, Raj Kumar [1 ]
Srinivas, K. [1 ]
Madhukar, G. [1 ]
机构
[1] CMR Tech Campus, Hyderabad, India
关键词
Ant colony optimizer; Gaussian mixture model; Histogram equalization; Histogram of oriented gradients; Local ternary pattern; Stack autoencoder; Vehicle type classification; GAUSSIAN MIXTURE MODEL; NETWORK;
D O I
10.1007/s11042-021-11508-5
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the intelligent transport system, vehicle type classification technology plays a major role. With the growth of video processing and pattern recognition application, a deep learning model is proposed in this research article to improve vehicle type classification under dynamic background. Initially, the original video sequences are collected from MIOvision Traffic Camera Dataset (MIO-TCD), and CDnet2014 dataset. Additionally, the contrast and visible level of the video frames are improved by implementing histogram equalization method. Next, the moving vehicles are detected and tracked using Gaussian Mixture Model (GMM) and Kalman filter. Then, the feature extraction is accomplished using Dual Tree Complex Wavelet Transform (DTCWT), Histogram of Oriented Gradients (HOG), and Local Ternary Pattern (LTP) to extract the texture feature vectors. Further, a new graph clustering-Ant Colony Optimization (ACO) algorithm is proposed to select the active feature vectors for better vehicle type classification. Lastly, the selected active feature vectors are given as the input to stack autoencoder classifier to classify eleven vehicle types in MIO-TCD and four vehicle types in CDnet2014 dataset. In the experimental section, the graph ACO based stack autoencoder model achieved 99.09%, and 89.89% of classification accuracy on both MIO-TCD, and CDnet2014 dataset, which are better related to the existing models like attention based method, improved spatiotemporal sample consistency algorithm, and generative adversarial nets.
引用
收藏
页码:42163 / 42182
页数:20
相关论文
共 50 条
  • [21] Chinese Text Classification Based on Ant Colony Optimization
    Luo Xin
    PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON MECHATRONICS, MATERIALS, CHEMISTRY AND COMPUTER ENGINEERING 2015 (ICMMCCE 2015), 2015, 39 : 51 - 54
  • [22] Instance-based classification with Ant Colony Optimization
    Salama, Khalid M.
    Abdelbar, Ashraf M.
    Helal, Ayah M.
    Freitas, Alex A.
    INTELLIGENT DATA ANALYSIS, 2017, 21 (04) : 913 - 944
  • [23] Chinese Text Classification Based on Ant Colony Optimization
    Luo Xin
    PROCEEDINGS OF THE 2015 4TH NATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS AND COMPUTER ENGINEERING ( NCEECE 2015), 2016, 47 : 37 - 41
  • [24] Graph attention autoencoder inspired CNN based brain tumor classification using MRI
    Mishra, Lalita
    Verma, Shekhar
    NEUROCOMPUTING, 2022, 503 : 236 - 247
  • [25] Eectric Vehicle Crying Scheduling Using Ant Colony System
    Mavrovouniotis, Michalis
    Ellinas, Georgios
    Polycarpou, Marios
    2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2019, : 2581 - 2588
  • [26] An Ant Colony Algorithm Assisted by Graph Neural Networks for Solving Vehicle Routing Problems
    Wang, Xiangyu
    Jin, Yaochu
    PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2023 COMPANION, 2023, : 5 - 6
  • [27] An improved ant colony algorithm based on Vehicle Routing Problem
    Pan, Tinglei
    Pan, Haipeng
    Gao, Jingfei
    2015 34TH CHINESE CONTROL CONFERENCE (CCC), 2015, : 2747 - 2752
  • [28] Hybrid ant lion mutated ant colony optimizer technique for Leukemia prediction using microarray gene data
    D. Santhakumar
    S. Logeswari
    Journal of Ambient Intelligence and Humanized Computing, 2021, 12 : 2965 - 2973
  • [29] Hybrid ant lion mutated ant colony optimizer technique for Leukemia prediction using microarray gene data
    Santhakumar, D.
    Logeswari, S.
    JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2021, 12 (02) : 2965 - 2973
  • [30] Network Link Status Classification Method Based on Graph Autoencoder
    Feng, Guoli
    Wang, Ning
    Ma, Run
    Wei, Wenbin
    Li, Xiaobo
    Lin, Peng
    EMERGING NETWORKING ARCHITECTURE AND TECHNOLOGIES, ICENAT 2022, 2023, 1696 : 405 - 416