Efficient Face Detection Based Crowd Density Estimation using Convolutional Neural Networks and an Improved Sliding Window Strategy

Buades, A., Coll, B. and Morel, J.-M. (2011). Non-local means denoising, Image Processing On Line 1: 208–212, DOI: 10.5201/ipol.2011.bcm_nlm. Apri DOISearch in Google Scholar

Cevikalp, H. and Triggs, B. (2012). Efficient object detection using cascades of nearest convex model classifiers, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, USA, pp. 3138–3145. Search in Google Scholar

Chan, A.B., Liang, Z.-S.J. and Vasconcelos, N. (2008). Privacy preserving crowd monitoring: Counting people without people models or tracking, 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, USA, pp. 1–7. Search in Google Scholar

Chen, D., Ren, S., Wei, Y., Cao, X. and Sun, J. (2014). Joint cascade face detection and alignment, in D. Fleet et al., (Eds), Computer Vision—ECCV 2014, Springer, Cham, pp. 109–122. Search in Google Scholar

Chen, J., Deng, Y., Bai, G. and Su, G. (2015). Face image quality assessment based on learning to rank, IEEE Signal Processing Letters 22(1): 90–94. Search in Google Scholar

Chen, K., Loy, C.C., Gong, S. and Xiang, T. (2012). Feature mining for localised crowd counting, British Machine Vision Conference (BMVC), Surrey, UK. Search in Google Scholar

Conte, D., Foggia, P., Percannella, G., Tufano, F. and Vento, M. (2010). A method for counting people in crowded scenes, 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, Boston, USA, pp. 225–232. Search in Google Scholar

Davies, A.C., Yin, J.H. and Velastin, S.A. (1995). Crowd monitoring using image processing, Electronics Communication Engineering Journal 7(1): 37–47. Search in Google Scholar

Face++ (2015). Face detection software, http://www.faceplusplus.com. Search in Google Scholar

Ferryman, J. and Ellis, A.-L. (2010). PETS2010: Dataset and challenge, 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, Boston, USA, Vol. 1, pp. 143–150. Search in Google Scholar

Fradi, H. and Dugelay, J. (2012). Low level crowd analysis using frame-wise normalized feature for people counting, 2012 IEEE International Workshop on Information Forensics and Security (WIFS), Costa Adeje, Spain, pp. 246–251. Search in Google Scholar

Ghiasi, G. and Fowlkes, C. (2014). Occlusion coherence: Localizing occluded faces with a hierarchical deformable part model, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Columbus, USA, pp. 1899–1906. Search in Google Scholar

Gourier, N., Hall, D. and Crowley, J.L. (2004). Estimating face orientation from robust detection of salient facial features, ICPR International Workshop on Visual Observation of Deictic Gestures, Cambridge, UK, pp. 17–25. Search in Google Scholar

Han, D., Kim, J., Ju, J., Lee, I., Cha, J. and Kim, J. (2014). Efficient and fast multi-view face detection based on feature transformation, 16th International Conference on Advanced Communication Technology, Pyeongchang, Korea (South), pp. 682–686. Search in Google Scholar

Idrees, H., Saleemi, I., Seibert, C. and Shah, M. (2013). Multi-source multi-scale counting in extremely dense crowd images, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, USA, pp. 2547–2554. Search in Google Scholar

Ioffe, S. and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift, CoRR: abs/1502.03167, http://arxiv.org/abs/1502.03167. Search in Google Scholar

Jiang, H. and Learned-Miller, E. (2017). Face detection with the faster r-CNN, 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017), Washington, USA, pp. 650–657. Search in Google Scholar

Jones, M. and Viola, P. (2003). Fast multi-view face detection, Technical Report TR2003-96, MERL—Mitsubishi Electric Research Laboratories, Cambridge, http://www.merl.com/publications/TR2003-96/. Search in Google Scholar

Keras (2015). Keras extras code, https://github.com/keras-team/keras. Search in Google Scholar

Khatoon, R., Saqlain, S.M. and Bibi, S. (2012). A robust and enhanced approach for human detection in crowd, 2012 15th International Multitopic Conference (INMIC), Islamabad, Pakistan, pp. 215–221. Search in Google Scholar

Kian Ara, R. and Matiolanski, A. (2019). AGH Crowd Density Estimation Database (ACD), http://kt.agh.edu.pl/matiolanski/CrowdDensityEstimationDatabase/. Search in Google Scholar

King, D.E. (2009). Dlib-ml: A machine learning toolkit, Journal of Machine Learning Research 10: 1755–1758. Search in Google Scholar

King, D.E. (2015). Max-margin object detection, CoRR: abs/1502.00046, http://arxiv.org/abs/1502.00046. Search in Google Scholar

Kong, D., Gray, D. and Tao, H. (2005). Counting pedestrians in crowds using viewpoint invariant training, BMVC 2005— Proceedings of the British Machine Vision Conference, Oxford, UK. Search in Google Scholar

Kotan, M., Öz, C. and Kahraman, A. (2021). A linearization-based hybrid approach for 3D reconstruction of objects in a single image, International Journal of Applied Mathematics and Computer Science 31(3): 501–513, DOI: 10.34768/amcs-2021-0034. Apri DOISearch in Google Scholar

Li, J. and Zhang, Y. (2013). Learning surf cascade for fast and accurate object detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, USA, pp. 3468–3475. Search in Google Scholar

Li, S.Z., Zhu, L., Zhang, Z., Blake, A., Zhang, H. and Shum, H. (2002). Statistical learning of multi-view face detection, in A. Heyden et al. (Eds), Computer Vision—ECCV 2002, Springer, Berlin, pp. 67–81. Search in Google Scholar

Liao, S., Jain, A.K. and Li, S.Z. (2016). A fast and accurate unconstrained face detector, IEEE Transactions on Pattern Analysis and Machine Intelligence 38(2): 211–223. Search in Google Scholar

Liu, S., Dong, Y., Liu, W. and Zhao, J. (2012). Multi-view face detection based on cascade classifier and skin color, 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, Hangzhou, China, Vol. 01, pp. 56–60. Search in Google Scholar

Ma, R., Li, L., Huang, W. and Tian, Q. (2004). On pixel count based crowd density estimation for visual surveillance, IEEE Conference on Cybernetics and Intelligent Systems, 2004, Singapore, Vol. 1, pp. 170–173 vol.1. Search in Google Scholar

Mahbub, U., Patel, V., Chandre, D., Barbello, B. and Chellappa, R. (2016). Partial face detection for continuous authentication, CoRR: abs/1603.09364, https://arxiv.org/abs/1603.09364. Search in Google Scholar

Marin-Jimenez, M., Zisserman, A. and Ferrari, V. (2011). “Here’s looking at you, kid”. Detecting people looking at each other in videos, Proceedings of the British Machine Vision Conference, Dundee, UK, pp. 1–12, DOI: 10.5244/C.25.22. Apri DOISearch in Google Scholar

Najibi, M., Samangouei, P., Chellappa, R. and Davis, L.S. (2017). SSH: Single stage headless face detector, 2017 IEEE International Conference on Computer Vision (ICCV), Los Alamitos, USA, pp. 4885–4894. Search in Google Scholar

Open CV (2019). Open Source Computer Vision Library, https://github.com/opencv/opencv. Search in Google Scholar

Opitz, M., Waltner, G., Poier, G., Possegger, H. and Bischof, H. (2016). Grid loss: Detecting occluded faces, CoRR: abs/1609.00129, http://arxiv.org/abs/1609.00129. Search in Google Scholar

Orozco, J., Martineza, B. and Pantic, M. (2015). Empirical analysis of cascade deformable models for multi-view face detection, Image and Vision Computing 42: 47–61. Search in Google Scholar

Pai, Y.-T., Ruan, S.-J., Shie, M.-C. and Liu, Y.-C. (2006). A simple and accurate color face detection algorithm in complex background, 2006 IEEE International Conference on Multimedia and Expo, Toronto, Canada, Vol. 2006, pp. 1545–1548. Search in Google Scholar

Ren, S., He, K., Girshick, R.B. and Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks, CoRR: abs/1506.01497, http://arxiv.org/abs/1506.01497. Search in Google Scholar

Rosebrock, A. (2021). MiniVGGNet: Going deeper with CNNs, https://pyimagesearch.com/2021/05/22/minivggnet-going-deeper-with-cnns/. Search in Google Scholar

Saleh, S.A.M., Suandi, S.A. and Ibrahim, H. (2015). Recent survey on crowd density estimation and counting for visual surveillance, Engineering Applications of Artificial Intelligence 41: 103–114. Search in Google Scholar

Shapiro, L.G. and Stockman, G.C. (2001). Computer Vision, Prentice Hall PTR, Upper Saddle River, pp. 137–150. Search in Google Scholar

Sheng-Fuu, L., Jaw-Yeh, C. and Hung-Xin, C. (2001). Estimation of number of people in crowded scenes using perspective transformation, IEEE Transactions on Systems, Man, and Cybernetics A: Systems and Humans 31(6): 645–654. Search in Google Scholar

Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition, arXiv: 1409.1556, https://arxiv.org/abs/1409.1556. Search in Google Scholar

Sun, X., Wu, P. and Hoi, S. (2017). Face detection using deep learning: An improved faster RCNN approach, Neurocomputing 299: 42–50. Search in Google Scholar

Thomaz, C. and Giraldi, G. (2010). A new ranking method for principal components analysis and its application to face image analysis, Image and Vision Computing 28(6): 902–913. Search in Google Scholar

Tsai, Y.-H., Lee, Y.-C., Ding, J.-J., Y. Chang, R. and Hsu, M.-C. (2018). Robust in-plane and out-of-plane face detection algorithm using frontal face detector and symmetry extension, Image and Vision Computing 78: 26–41. Search in Google Scholar

Viola, P. and Jones, M.J. (2004). Robust real-time face detection, International Journal of Computer Vision 57(2): 137–154, DOI: 10.1023/B:VISI.0000013087.49260.fb. Apri DOISearch in Google Scholar

Wan, S., Chen, Z., Zhang, T., Zhang, B. and Wong, K. (2016). Bootstrapping face detection with hard negative examples, CoRR: abs/1608.02236, http://arxiv.org/abs/1608.02236. Search in Google Scholar

Yang, B., Yan, J., Lei, Z. and Li, S.Z. (2014). Aggregate channel features for multi-view face detection, CoRR: abs/1407.4023, http://arxiv.org/abs/1407.4023. Search in Google Scholar

Yang, S., Luo, P., Loy, C.C. and Tang, X. (2015). From facial parts responses to face detection: A deep learning approach, 2015 IEEE International Conference on Computer Vision (ICCV), Las Condes, Chile, pp. 3676–3684. Search in Google Scholar

You-jia, F. and Jian-wei, L. (2010). Rotation invariant multi-view color face detection based on skin color and ADaBOost algorithm, 2015 IEEE International Conference on Computer Vision (ICCV), Wuhan, China. Search in Google Scholar

Zhao, T., Nevatia, R. and Wu, B. (2008). Segmentation and tracking of multiple humans in crowded environments, IEEE Transactions on Pattern Analysis and Machine Intelligence 30(7): 1198–1211. Search in Google Scholar

Zhu, Q., Yeh, M.-C., Cheng, K.-T.T. and Avidan, S. (2006). Fast human detection using a cascade of histograms of oriented gradients, IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2, New York, USA, pp. 1491–1498. Search in Google Scholar

Zhu, X. and Ramanan, D. (2012). Face detection, pose estimation, and landmark localization in the wild, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, USA, pp. 2879–2886. Search in Google Scholar

Zitouni, M.S. and Śluzek, A. (2022). A data association model for analysis of crowd structure, International Journal of Applied Mathematics and Computer Science 32(1): 81–94, DOI: 10.34768/amcs-2022-0007. Apri DOISearch in Google Scholar