MHAMD-MST-CNN: multiscale head attention guided multiscale density maps fusion for video crowd counting via multi-attention spatial-temporal CNN

References

• Amrani M, Bey A, Amamra A. 2021. New SAR target recognition based on YOLO and very deep multi-canonical correlation analysis. Int J Remote Sens. 00(00). Taylor & Francis:1–20. doi:10.1080/01431161.2021.1953719.
   Google Scholar
• Amrani M, Hammad M, Jiang F, Wang K, Amrani A. 2018. Very deep feature extraction and fusion for arrhythmias detection. Neural Comput Appl. 30(7). Springer London: 2047–2057. doi:10.1007/s00521-018-3616-9.
   Google Scholar
• Basalamah S, Daud Khan S, Ullah H. 2019. Scale driven convolutional neural network model for people counting and localization in crowd scenes. IEEE Access. 7 IEEE:71576–71584. doi:10.1109/ACCESS.2019.2918650.
   Google Scholar
• Boominathan L. 2016. “CrowdNet: a deep convolutional network for dense crowd counting.” In Proceedings of the 24th ACM International Conference on Multimedia; Netherlands, p. 640–644.
   Google Scholar
• Cao X, Wang Z, Zhao Y, Su F. 2018. Scale aggregation network for accurate and efficient crowd counting. Lect Notes Comput Sci (Incl Subseries Lect Notes Artif Intell Lect Notes Bioinf). 11209 LNCS: 757–773. doi:10.1007/978-3-030-01228-1_45
   Google Scholar
• Chan AB, Vasconcelos N. 2012. Counting people with low-level features and bayesian regression. IEEE Trans Image Process. 21(4). IEEE:2160–2177. doi:10.1109/TIP.2011.2172800.
   Google Scholar
• Chen K, Change Loy C, Gong S, Xiang T. 2012. Feature mining for localised crowd counting. BMVC. 1(2):1–11.
   Google Scholar
• Chenchu X, Gao Z, Zhang H, Shuo L, de Albuquerque VHC. 2021. Video salient object detection using dual-stream spatiotemporal attention. Appl Soft Comput. 108 Elsevier B.V.:107433. doi:10.1016/j.asoc.2021.107433.
   Google Scholar
• Daniel OR, López-Sastre RJ. 2016. “Towards perspective-free object counting with deep learning.” European Conference on Computer Vision. Springer, Cham; 615–629. doi:10.1007/978-3-319-46478-7_38.
   Google Scholar
• Deb D, Ventura J. 2018. “An aggregated multicolumn dilated convolution network for perspective-free counting.” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2018-June: 308–317. doi:10.1109/CVPRW.2018.00057.
   Google Scholar
• Han K, Wan W, Yao H, Hou L. 2017. Image crowd counting using convolutional neural network and Markov random field. 1–6.
   Google Scholar
• Kang D, Chan A. 2019. “Crowd counting by adaptively fusing predictions from an image pyramid.” British Machine Vision Conference 2018, BMVC 2018; Newcastle, UK. p. 1–12.
   Google Scholar
• Khan SD, Basalamah S. 2021a. Scale and density invariant head detection deep model for crowd counting in pedestrian crowds. Vis Comput. 37(8). Springer Berlin Heidelberg: 8–21372127–2137. doi:10.1007/s00371-020-01974-7.
   Google Scholar
• Khan SD, Basalamah S. 2021b. Sparse to dense scale prediction for crowd counting in high density crowds. Arab J Sci Eng. 46(4). Springer Berlin Heidelberg:3051–3065. doi:10.1007/s13369-020-04990-w.
   Google Scholar
• Khan SD, Saleh Y, Zafar B, Noorwali A. 2021. A deep-fusion network for crowd counting in high-density crowded scenes. Int J Comput Intell Syst. 14(1). Springer Netherlands:1–12. doi:10.1007/s44196-021-00016-x.
   Google Scholar
• Kingma DP, Jimmy B 2014. “Adam: a method for stochastic optimization,” 1–15. http://arxiv.org/abs/1412.6980.
   Google Scholar
• Lempitsky V, Zisserman A. 2010. Learning to count objects in images. Adv Neural Inf Process Syst. 3(3):1–5.
   Google Scholar
• Liu W, Salzmann M, Fua P. 2019. “Context-aware crowd counting.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2019-June: 5094–5103. doi:10.1109/CVPR.2019.00524.
   Google Scholar
• Liu X, Sang J, Weiqun W, Liu K, Liu Q, Xia X. 2021. Density-aware and background-aware network for crowd counting via multi-task learning. Pattern Recognit Lett. 150 Elsevier B.V.:221–227. doi:10.1016/j.patrec.2021.07.013.
   Google Scholar
• Miao Y, Han J, Gao Y, Zhang B. 2019. ST-CNN: spatial-temporal convolutional neural network for crowd counting in videos. Pattern Recognit Lett. 125 Elsevier B.V.: 113–118. doi:10.1016/j.patrec.2019.04.012.
   Google Scholar
• Mingliang X, Zhaoyang G, Jiang X, Cui G, Pei L, Zhou B, Changsheng X. 2019. Depth information guided crowd counting for complex crowd scenes. Pattern Recognit Lett. 125:563–569. doi:10.1016/j.patrec.2019.02.026.
   Google Scholar
• Pham VQ, Kozakaya T, Yamaguchi O, Okada R. 2015. “COUNT forest: co-voting uncertain number of targets using random forest for crowd density estimation.” Proceedings of the IEEE International Conference on Computer Vision 2015 Inter: 3253–3261. doi:10.1109/ICCV.2015.372.
   Google Scholar
• Qingqing X, Zhu Z, Huilin G, Zhang Z, Zang X. 2021. Effective face detector based on YOLOv5 and superresolution reconstruction. Comput Math Methods Med. 2021:1–9. doi:10.1155/2021/7748350.
   Google Scholar
• Rumelhart DE, Hinton GE, Williams RJ. 1986. ‘Learning Representations by Back-Propagating Errors.’. Nature. 323(6088):6088. doi:10.1038/323533a0.
   Google Scholar
• Sam DB, Surya S, Venkatesh Babu R. 2017a. “Switching convolutional neural network for crowd counting.” Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 2017-Janua: 4031–4039. doi:10.1109/CVPR.2017.429.
   Google Scholar
• Sam DB, Surya S, Venkatesh Babu R. 2017b. “Switching convolutional neural network for crowd counting.” Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 2017-Janua: 4031–4039. doi:10.1109/CVPR.2017.429.
   Google Scholar
• Shang C, Haizhou A, Bai B. 2016. “End-to-end crowd counting via joint learning local and global count.” Proceedings - International Conference on Image Processing, ICIP 2016-Augus. IEEE: 1215–1219. doi:10.1109/ICIP.2016.7532551.
   Google Scholar
• Shi X, Xin L, Caili W, Kong S, Yang J, Liang H 2020. “A real-time deep network for crowd counting.” http://arxiv.org/abs/2002.06515.
   Google Scholar
• Shi Z, Zhang L, Sun Y, Yangdong Y. 2018. Multiscale multitask deep NetVLAD for crowd counting. IEEE Trans Ind Inf. 14(11). IEEE:4953–4962. doi:10.1109/TII.2018.2852481.
   Google Scholar
• Sindagi VA, Patel VM. 2017. “CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting.” 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017. doi:10.1109/AVSS.2017.8078491.
   Google Scholar
• Sindagi VA, Patel VM. 2020. HA-CCN: hierarchical attention-based crowd counting network. IEEE Trans Image Process. 29(8). IEEE:323–335. doi:10.1109/TIP.2019.2928634.
   Google Scholar
• Tripathy SK, Kostha H, Srivastava R. 2022. TS-MDA: two-stream multiscale deep architecture for crowd behavior prediction. Multimedia Syst. 29(1):15–31. doi:10.1007/s00530-022-00975-x.
   Google Scholar
• Tripathy SK, Srivastava R. 2021. AMS-CNN: attentive multi-stream CNN for video-based crowd counting. Int J Multimedia Inf Retr. 10(4). Springer London: 239–254. doi:10.1007/s13735-021-00220-7.
   Google Scholar
• Tripathy SK, Srivastava R. 2022. “A novel deep architecture for multi-task crowd analysis.” In 2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 1–6. doi:10.1109/CONECCT55679.2022.9865100.
   Google Scholar
• Wang P, Gao C, Wang Y, Hui L, Gao Y. 2020. MobileCount: an efficient encoder-decoder framework for real-time crowd counting. Neurocomputing. 407:292–299. doi:10.1016/j.neucom.2020.05.056.
   Google Scholar
• Wang Y, Shiyu H, Wang G, Chen C, Pan Z. 2020. Multi-scale dilated convolution of convolutional neural network for crowd counting. Multimed Tools Appl. 79(1–2). Multimedia Tools and Applications: 1–2–10731057–1073. doi:10.1007/s11042-019-08208-6.
   Google Scholar
• Wang L, Yin B, Tang X, Yun L. 2019. Removing background interference for crowd counting via de-background detail convolutional network. Neurocomputing. 332 Elsevier B.V.:360–371. doi:10.1016/j.neucom.2018.12.047.
   Google Scholar
• Wang Y, Zhang W, Liu Y, Zhu J. 2020. Multi-density map fusion network for crowd counting. Neurocomputing. 397 Elsevier B.V.: 31–38. doi:10.1016/j.neucom.2020.02.010.
   Google Scholar
• Wang C, Zhang H, Yang L, Liu S, Cao X. 2015. “Deep people counting in extremely dense crowds.” In MM 2015 - Proceedings of the 2015 ACM Multimedia Conference, 1299–1302. doi:10.1145/2733373.2806337.
   Google Scholar
• Wei X, Junping D, Liang M, Lingfei Y. 2019. Boosting deep attribute learning via support vector regression for fast moving crowd counting. Pattern Recognit Lett. 119 Elsevier B.V.: 119–2312–23. doi:10.1016/j.patrec.2017.12.002.
   Google Scholar
• Xiong F, Shi X, Yan Yeung D. 2017. “Spatiotemporal modeling for crowd counting in videos.” Proceedings of the IEEE International Conference on Computer Vision 2017-Octob: 5161–5169. doi:10.1109/ICCV.2017.551.
   Google Scholar
• Yuhong L, Zhang X, Chen D. 2018. “Csrnet: dilated convolutional neural networks for understanding the highly congested scenes.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1091–1100. doi:10.1109/CVPR.2018.00120.
   Google Scholar
• Zeng L, Xiangmin X, Cai B, Qiu S, Zhang T. 2017. “Multi-scale convolutional neural networks for crowd counting.” In 2017 IEEE International Conference on Image Processing (ICIP); Beijing, China. p. 465–469.
   Google Scholar
• Zhang S, Guanhang W. 2017. FCN-RLSTM: deep spatio-temporal neural networks for. Iccv. 3687–3696.
   Google Scholar
• Zhang C, Hongsheng L, Wang X, Yang X. 2015. “Cross-scene crowd counting via deep convolutional neural networks.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 07-12-June: 833–841. doi:10.1109/CVPR.2015.7298684.
   Google Scholar
• Zhang L, Lin L, Liang X, Kaiming H 2016. “Is faster R-CNN doing well for pedestrian detection?” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9906 LNCS: 443–457. doi:10.1007/978-3-319-46475-6_28.
   Google Scholar
• Zhang L, Shi M, Chen Q. 2018. “Crowd counting via scale-adaptive convolutional neural network.” Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018 2018-Janua (1). IEEE: 1113–1121. doi:10.1109/WACV.2018.00127.
   Google Scholar
• Zhang Y, Zhou D, Chen S, Gao S, Ma Y. 2016. “Single-image crowd counting via multi-column convolutional neural network.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 589–597. doi:10.1002/slct.201701956.
   Google Scholar
• Zhou Y, Yang J, Hongru L, Cao T, Kung SY. 2020. “Adversarial learning for multiscale crowd counting under complex scenes.” IEEE Transactions on Cybernetics. IEEE, 1–10. doi:10.1109/TCYB.2019.2956091.
   Google Scholar
• Zou Z, Xinxing S, Xiaoye Q, Zhou P. 2018. DA-Net: learning the fine-grained density distribution with deformation aggregation network. IEEE Access. 6:60745–60756. doi:10.1109/ACCESS.2018.2875495.
   Google Scholar