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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References
Chen, X., Henrickson, K., Wang, Y.: Kinect-based pedestrian detection for crowded scenes. Comput. Aided Civil Infrastruct. Eng. 31(3), 229–240 (2016)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). (2005). IEEE
Felzenszwalb, P.F., et al.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2009)
Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). (2005). IEEE
Zhao, T., Nevatia, R., Wu, B.: Segmentation and tracking of multiple humans in crowded environments. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1198–1211 (2008)
Lempitsky, V., Zisserman, A.: Learning to count objects in images. In: Advances in Neural Information Processing Systems (2010)
Sam, D.B., Surya, S., Babu, R.V.: Switching convolutional neural network for crowd counting. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (2017). IEEE
Xu, B., Qiu, G.: Crowd density estimation based on rich features and random projection forest. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). (2016). IEEE
Davies, A.C., Yin, J.H., Velastin, S.A.: Crowd monitoring using image processing. Electron. Commun. Eng. J. 7(1), 37–47 (1995)
Alanazi, A.A., Bilal, M.: Crowd density estimation using novel feature descriptor. arXiv preprint arXiv:1905.05891 (2019)
Lamba, S., Nain, N.: A texture based mani-fold approach for crowd density estimation using Gaussian Markov Random Field. Multimedia Tools Appl. 78(5), 5645–5664 (2019)
Liu, J., et al.: Decidenet: Counting varying density crowds through attention guided detection and density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2018)
Dollar, P., et al.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743–761 (2011)
Chan, A.B., Vasconcelos, N.: Bayesian poisson regression for crowd counting. In: 2009 IEEE 12th international conference on computer vision (2009) IEEE
Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. Int. J. Comput. Vision 63(2), 153–161 (2005)
Marana, A.N., et al.: Estimation of crowd density using image processing. (1997)
Saqib, M., Khan, S.D., Blumenstein, M.: Texture-based feature mining for crowd density estimation: a study. In: 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ). (2016). IEEE
Gong, S., Bourennane, E.-B.: A method based on texture feature and edge detection for people counting in a crowded area. (2019)
Pham, V.-Q., et al.: Count forest: co-voting uncertain number of targets using random forest for crowd density estimation. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vision 57(2), 137–154 (2004)
Wu, B., Nevatia, R.: Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. In: Tenth IEEE International Conference on Computer Vision (ICCV’05) Volume 1. (2005). IEEE
Sabzmeydani, P., Mori, G.: Detecting pedestrians by learning shapelet features. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition. (2007). IEEE
Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors. Int. J. Comput. Vision 75(2), 247–266 (2007)
Gong, S., Bourennane, E.-B.: A method based on multi-source feature detection for counting people in crowded areas. (2019)
Meynberg, O., Cui, S., Reinartz, P.: Detection of high-density crowds in aerial images using texture classification. Remote Sens. 8(6), 470 (2016)
Saleem, M.S., et al.: Crowd density estimation in still images using multiple local features and boosting regression ensemble. Neural Comput. Appl. 5, 1–10 (2019)
Hussain, N., et al.: CDES: a pixel-based crowd density estimation system for Masjid al-Haram. Saf. Sci. 49(6), 824–833 (2011)
Chan, A.B., Liang, Z.-S.J., Vasconcelos, N.: Privacy preserving crowd monitoring: Counting people without people models or tracking. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition. (2008). IEEE
Marana, A., et al.: On the efficacy of texture analysis for crowd monitoring. In: Proceedings SIBGRAPI’98. International Symposium on Computer Graphics, Image Processing, and Vision (Cat. No. 98EX237). (1998). IEEE
Wu, X., et al.: Crowd density estimation using texture analysis and learning. In: 2006 IEEE International Conference on Robotics and Biomimetics. (2006). IEEE
Pai, A.K., Karunakar, A., Raghavendra, U.: A novel crowd density estimation technique using local binary pattern and Gabor features. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). (2017). IEEE
Choudhary, S., Ojha, N., Singh, V.: Real-time crowd behavior detection using SIFT feature extraction technique in video sequences. In: 2017 International Conference on Intelligent Computing and Control Systems (ICICCS). (2017). IEEE
Nagao, K., Yanagisawa, D., Nishinari, K.: Estimation of crowd density applying wavelet transform and machine learning. Phys. A 510, 145–163 (2018)
Ma, W., Huang, L., Liu, C.: Advanced local binary pattern descriptors for crowd estimation. In: 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application. (2008). IEEE
Wang, Z., et al.: Crowd density estimation based on local binary pattern co-occurrence matrix. In: 2012 IEEE International Conference on Multimedia and Expo Workshops. (2012). IEEE
Lloyd, K., et al.: Detecting violent and abnormal crowd activity using temporal analysis of grey level co-occurrence matrix (GLCM)-based texture measures. Mach. Vis. Appl. 28(3–4), 361–371 (2017)
Lloyd, K., et al.: Detecting violent crowds using temporal analysis of GLCM texture. arXiv preprint arXiv:1605.05106 (2016)
Balbin, J.R., et al.: Crowd counting system by facial recognition using Histogram of Oriented Gradients, Completed Local Binary Pattern, Gray-Level Co-Occurrence Matrix and Unmanned Aerial Vehicle. In: Third International Workshop on Pattern Recognition. (2018). International Society for Optics and Photonics
Ma, W., Huang, L., Liu, C.: Crowd density analysis using co-occurrence texture features. In: 5th International Conference on Computer Sciences and Convergence Information Technology. (2010). IEEE
Lamba, S., Nain, N.: Multi-source approach for crowd density estimation in still images. In: 2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA). (2017). IEEE
Wang, C., et al.: Deep People Counting in Extremely Dense Crowds. In: Proceedings of the 23rd ACM International Conference on MULTIMEDIA. (2015), Association for Computing Machinery: Brisbane, Australia, pp. 1299–1302
Zhang, C., et al.: Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2015)
Sindagi, V.A., Patel, V.M.: Generating high-quality crowd density maps using contextual pyramid cnns. In: Proceedings of the IEEE International Conference on Computer Vision. (2017)
Kumagai, S., Hotta, K., Kurita, T.: Mixture of counting cnns: adaptive integration of cnns specialized to specific appearance for crowd counting. arXiv preprint arXiv:1703.09393 (2017)
Shang, C., Ai, H., Bai, B.: End-to-end crowd counting via joint learning local and global count. In: 2016 IEEE International Conference on Image Processing (ICIP). (2016). IEEE
Cui, X., et al.: Abnormal detection using interaction energy potentials. In: CVPR 2011. (2011). IEEE
Hassner, T., Itcher, Y., Kliper-Gross, O.: Violent flows: real-time detection of violent crowd behavior. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. (2012). IEEE
Sadlier, D.A., O’Connor, N.E.: Event detection in field sports video using audio-visual features and a support vector machine. IEEE Trans. Circuits Syst. Video Technol. 15(10), 1225–1233 (2005)
Kim, D., et al.: Crowd density estimation using multi-class adaboost. In: 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance. (2012). IEEE
Fradi, H., Zhao, X., Dugelay, J.-L.: Crowd density analysis using subspace learning on local binary pattern. In: 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). (2013). IEEE
Xu, D., et al.: Learning deep representations of appearance and motion for anomalous event detection. arXiv preprint arXiv:1510.01553 (2015)
Rahmalan, H., Nixon, M.S., Carter, J.N.: On crowd density estimation for surveillance. (2006)
Zhou, B., et al.: Multilinear rank support tensor machine for crowd density estimation. Eng. Appl. Artif. Intell. 72, 382–392 (2018)
Gan, J., et al.: K-means based on active learning for support vector machine. In: 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS). (2017). IEEE
Moghaddass, R., Sheng, S.: An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework. Appl. Energy 240, 561–582 (2019)
Polus, A., Schofer, J.L., Ushpiz, A.: Pedestrian flow and level of service. J. Transp. Eng. 109(1), 46–56 (1983)
Hussain, M., et al.: Comparison between WLD and LBP descriptors for non-intrusive image forgery detection. In: 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings. (2014). IEEE
Xia, Z., et al.: A novel weber local binary descriptor for fingerprint liveness detection. IEEE Trans. Syst. Man Cybern. Syst. 5, 94 (2018)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Hou, Y.L., Pang, G.K.H.: Human detection in crowded scenes. (2010)
Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 6, 610–621 (1973)
Karaãalä, B., Ramanath, R., Snyder, W.E.: A comparative analysis of structural risk minimization by support vector machines and nearest neighbor rule. Pattern Recogn. Lett. 25(1), 63–71 (2004)
Ding, S., Qi, B., Tan, H.: Research review of support vector machine theory and algorithm. J. Univ. Electron. Sci. Technol. China 40(1), 2–10 (2011)
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory. (1992)
Li, P., Xu, S.: Analysis of kernel function characteristics of support vector machine in pattern recognition. Comput. Eng. Des. 26(2), 302–304 (2005)
Zhang, Y., et al.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2016)
Idrees, H., et al.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2013)
Xu, X., Zhang, D., Zheng, H.: Crowd density estimation of scenic spots based on multifeature ensemble learning. J. Electr. Comput. Eng. 2, 73–113 (2017)
Walach, E., Wolf, L.: Learning to count with cnn boosting. In: European Conference on Computer Vision. (2016). Springer
Rodriguez, M., et al.: Density-aware person detection and tracking in crowds. In: 2011 International Conference on Computer Vision. (2011). IEEE
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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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DOI: https://doi.org/10.1007/s00138-021-01167-9