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Crowd density classification method based on pixels and texture features

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Abstract

Crowd density classification has been a challenging task in the field of computer vision, which has various applications in public and commercial domains. Many researches on the classification and recognition method of the crowd density have been introduced in the past, while there still exists the problems of inaccuracy, poor robustness and inefficiency. An adaptive crowd density classification method based on pixels and texture features is proposed in this paper. Core part of the method is to adopt different processing methods according to the corresponding crowd density. The method based on pixel regression method is used for the sparse crowd condition, while the texture features are applied in the dense crowd. Variety of texture features like local binary pattern (LBP), gray-level co-occurrence matrix (GLCM), Gabor, Haar-like and Wavelet group are used on the WorldExpo’10 dataset to obtain an optimum combination of these features, which is proposed to extract the texture features of the crowd images. Then the SVM classifier model based on Bayesian estimation is adopted to train the model which can filter the abnormal sample data to improve the accuracy and generalization performance of the algorithm. Meanwhile, a K-means clustering iterative training method based on optimized sorting samples is designed to improve the training speed in the training process. Extensive experiments from various aspects including parameter optimization, feature selection and model evaluation were conducted. The performance of the model is tested based on mean absolute error (MAE), mean squared error (MSE) and classification rate (CR) in dataset UCSD, Shanghai Tech_A and UCF_CC_50. The experimental results show that CR of the proposed method can reach to 98.2%, whose indexes of MAE and MSE also outperform the most existing methods. In general, the proposed approach in this paper has obvious advantages and great application value.

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The research fund of this paper comes from the science and technology fund of Beijing Jiaotong University. The details and uses of the fund have been approved and checked. Corresponding author and other authors agree on this and know the relevant details.

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  1. School of Electronics and Information Engineering, Beijing Jiaotong University, No.3 Shangyuancun Haidian District, Beijing, 100044, China

    Dongyao Jia, Chuanwang Zhang & Bing Zhang

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  1. Dongyao Jia
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Correspondence to Chuanwang Zhang.

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Jia, D., Zhang, C. & Zhang, B. Crowd density classification method based on pixels and texture features. Machine Vision and Applications 32, 43 (2021). https://doi.org/10.1007/s00138-021-01167-9

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