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Detection of crowdedness in bus compartments based on ResNet algorithm and video images

  • 1193: Intelligent Processing of Multimedia Signals
  • Published:
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Abstract

The crowding in bus is an important factor affecting passenger satisfaction and bus dispatching level. However, how to use video images to detect crowding accurately is a difficult problem. In this paper, firstly, an image sample library is established based on the evaluation standard of crowding in bus, which contains 16346 sample images. Then, Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) are used to extract the texture features of the image in bus. Then, a rough classification method of crowding based on Support Vector Machine (SVM) is proposed. At the same time, in order to improve the accuracy of rough classification of crowding, the optimization effects of grid search algorithm, particle swarm optimization algorithm and genetic algorithm on SVM parameters are compared. The results show that the optimization effect of genetic algorithm is the best, and the accuracy rate is 93.20%. Finally, for the problem that the SVM method is not ideal in the fine classification of crowding, this paper proposes a new method based on ResNet. SGD, Adadelta and Adam are selected to optimize the parameters of ResNet model. The accuracy of the optimal Adam algorithm reaches 96.22%, which effectively solves the problem of the fine classification of crowding in bus.

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Acknowledgement

This work is supported by the National Key R&D Program of China (2019YFB1600200), National Natural Science Foundation of China (71871011, 71890972/71890970, 71621001), and the first batch of science and technology projects of Jingde Expressway (JD-202014).

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Authors and Affiliations

  1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, China

    Jiandong Zhao, Zijian Li & Dongfeng Zhao

  2. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing, 100044, China

    Jiandong Zhao

  3. Hebei Provincial Communication Planning and Design Institute, Shijiazhuang, 050000, China

    Wei Lei, Mingmin Han & Xiaoqing Hou

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  1. Jiandong Zhao
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  2. Wei Lei
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  3. Zijian Li
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  4. Dongfeng Zhao
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  5. Mingmin Han
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  6. Xiaoqing Hou
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Correspondence to Jiandong Zhao.

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Zhao, J., Lei, W., Li, Z. et al. Detection of crowdedness in bus compartments based on ResNet algorithm and video images. Multimed Tools Appl 81, 4753–4780 (2022). https://doi.org/10.1007/s11042-021-11008-6

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  • DOI: https://doi.org/10.1007/s11042-021-11008-6

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