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An interactive network based on transformer for multimodal crowd counting

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Abstract

Crowd counting is a task to estimate the total number of pedestrians in an image. In most of the existing research, good vision problems, such as in parks, squares, and bright shopping malls during the day, have been addressed. However, there is little research on complex scenes in darkness. To study this problem, we propose an interactive network based on Transformer for multi-modal crowd counting. First, sliding convolutional encoding is adopted for the image to obtain better encoding features. The features are extracted through the designed primary interaction network, and then channel token attention is used to modulate the features. Then, the FGAF-MLP is used for high and low semantic fusion to enhance the feature expression and fully fuse the data in different modes to improve the accuracy of the method. To verify the effectiveness of our method, we conducted extensive ablation experiments with the latest multimodal benchmark RGBT-CC, and we verified the complementarity between multiple modal data and the effectiveness of the model components. We also verified the effectiveness of our method with the ShanghaiTechRGBD benchmark. The experimental results showed that our proposed method exhibits good results and achieves an improvement of more than 10\(\%\) in terms of the mean average error and mean squared error for the RGBT-CC benchmark.

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References

  1. Kumar N, Raubal M (2021) Applications of deep learning in congestion detection, prediction and alleviation: A survey. Transp Res C Emerg Technol 133:103432. https://doi.org/10.1016/j.trc.2021.103432. Get rights and content

  2. Bamaqa A, Sedky M, Bosakowski T et al (2022) SIMCD: SIMulated crowd data for anomaly detection and prediction. Expert Syst Appl 203:117475. https://doi.org/10.1016/j.eswa.2022.117475. Get rights and content

  3. Fan Z, Zhang H, Zhang Z et al (2022) A survey of crowd counting and density estimation based on convolutional neural network. Neurocomputing 472:224–251. https://doi.org/10.1016/j.neucom.2021.02.103

    Article  Google Scholar 

  4. Topkaya I S, Erdogan H, Porikli F (2014) Counting people by clustering person detector outputs. In: Proc of the 11th IEEE Int Conf on Advanced Video and Signal Based Surveillance, IEEE, Piscataway, NJ, pp 313–318. https://doi.org/10.1109/AVSS.2014.6918687

  5. Idrees H, Saleemi I, Seibert C et al (2013) Multi-source multi-scale counting in extremely dense crowd images. In: Proc of the IEEE Conf on Computer Vision and Pattern Recognition. IEEE, Piscataway, NJ, pp 2547–2554. https://doi.org/10.1109/CVPR.2013.329

  6. Delussu R, Putzu L, Fumera G (2022) Scene-specific crowd counting using synthetic training images. Pattern Recog 124:108484. https://doi.org/10.1016/j.patcog.2021.108484

    Article  Google Scholar 

  7. Yue X, Zhang C, Fujita H et al (2021) Clothing fashion style recognition with design issue graph. Appl Intell 51(6):3548–3560. https://doi.org/10.1007/s10489-020-01950-7

    Article  Google Scholar 

  8. Lecun Y, Bottou L, Bengio Y et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  9. Yu Y, Zhu H, Wang L et al (2021) Dense crowd counting based on adaptive scene division. Int J Mach Learn Cybern 12(4):931–942. https://doi.org/10.1007/s13042-020-01212-5

    Article  Google Scholar 

  10. Liang L, Zhao H, Zhou F et al (2022) SC2Net: scale-aware crowd counting network with pyramid dilated convolution. Appl Intell 1–14. https://doi.org/10.1007/s10489-022-03648-4

  11. Wang K, Liu M (2022) YOLOv3-MT: A YOLOv3 using multi-target tracking for vehicle visual detection. Appl Intell 52(2):2070–2091. https://doi.org/10.1007/s10489-021-02491-3

    Article  Google Scholar 

  12. Xie J, Gu L, Li Z et al (2022) HRANet: Hierarchical region-aware network for crowd counting. Appl Intell 1–15. https://doi.org/10.1007/s10489-021-03030-w

  13. Wang W, Liu Q, Wang W (2022) Pyramid-dilated deep convolutional neural network for crowd counting. Appl Intell 52(2):1825–1837. https://doi.org/10.1007/s10489-021-02537-6

    Article  Google Scholar 

  14. Shi Y, Sang J, Wu Z et al (2022) MGSNet: A multi-scale and gated spatial attention network for crowd counting. Appl Intell 1–11. https://doi.org/10.1007/s10489-022-03263-3

  15. Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. Adv Neural Inf Process Syst 5998–6008. https://doi.org/10.1609/aaai.v34i07.6693

  16. Dosovitskiy A, Beyer L, Kolesnikov A et al (2021) An image is worth 16x16 words: Transformers for image recognition at scale. In: Proceedings of the International Conference on Learning Representations. https://openreview.net/forum?id=YicbFdNTTy. Accessed 13 Jan 2021

  17. Liu L, Chen J, Wu H et al (2021) Cross-modal collaborative representation learning and a large-scale RGBT benchmark for crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4823–4833. https://doi.org/10.1109/CVPR46437.2021.00479

  18. Dongze Lian, Jing Li, Jia Zheng, Weixin Luo, and Sheng hua Gao (2019) Density map regression guided detection network for RGB-D crowd counting and localization. In: CVPR, pp 1821–1830. https://doi.org/10.1109/CVPR.2019.00192

  19. Gavrila D M, Philomin V (1999) Real-time object detection for “smart” vehicles. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol 1. IEEE, Kyoto, pp 87–93. https://doi.org/10.1109/ICCV.1999.791202

  20. Zhang C, Li H , Wang X et al (2015) Cross-scene crowd counting via deep convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, Boston, pp 833–841. https://doi.org/10.1109/CVPR.2015.7298684

  21. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 1. IEEE, pp 886–893. https://doi.org/10.1109/CVPR.2005.177

  22. Yang S D, Su H T, Hsu W H et al (2019) DECCNet: Depth enhanced crowd counting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops. https://doi.org/10.1109/ICCVW.2019.00553

  23. Jiang X, Zhang L, Xu M et al (2020) Attention scaling for crowd counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4706–4715. https://doi.org/10.1109/CVPR42600.2020.00476

  24. Ma Z, Wei X, Hong X et al (2019) Bayesian loss for crowd count estimation with point supervision. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 6141–6150. https://doi.org/10.1109/ICCV.2019.00624. IEEE

  25. Carion N, Massa F, Synnaeve G et al (2020) End-to-end object detection with transformers. In: European Conference on Computer Vision, Springer, Cham, pp 213–229. https://doi.org/10.1007/978-3-030-58452-8_13

  26. He J, Chen JN, Liu S et al (2022) TransFG: A transformer architecture for fine-grained recognition. Proc AAAI Conf Artif Intel. 36(1):852–860. https://doi.org/10.1609/aaai.v36i1.19967

    Article  Google Scholar 

  27. Han K, Xiao A, Wu E et al (2021) Transformer in transformer. Adv Neural Inf Process Syst 34:15908–15919

    Google Scholar 

  28. Liu Z, Lin Y, Cao Y et al (2021) Swin Transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 10012–10022. https://doi.org/10.1109/ICCV48922.2021.00986

  29. Liang D, Chen X, Xu W et al (2022) Transcrowd: weakly-supervised crowd counting with transformers. Sci China Inf Sci 65(6):160104. https://doi.org/10.1007/s11432-021-3445-y

    Article  Google Scholar 

  30. Gao J, Gong M, Li X (2022) Congested crowd instance localization with dilated convolutional Swin transformer. Neurocomputing 513:94–103. https://doi.org/10.1016/j.neucom.2022.09.113

    Article  Google Scholar 

  31. Yuan L, Chen Y, Wang T et al (2021) Tokens-to-Token ViT: Training vision transformers from scratch on imagenet. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 558–567. https://doi.org/10.1109/ICCV48922.2021.00060

  32. Baltrušaitis T, Ahuja C, Morency L P. Multimodal machine learning: A survey and taxonomy. IEEE Trans Pattern Anal Mach Intell 41(2):423–443. https://doi.org/10.1109/TPAMI.2018.2798607

  33. Lu J, Batra D, Parikh D et al (2019) ViLBERT: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, pp 13–23. https://dl.acm.org/doi/10.5555/3454287.3454289. Curran Associates Inc., Red Hook, NY, United States

  34. Devlin J, Chang M W, Lee K, Toutanova K (2019) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp 4171–4186. https://doi.org/10.18653/v1/N19-1423. Association for Computational Linguistics

  35. Ayetiran EF (2022) Attention-based aspect sentiment classification using enhanced learning through CNN-BiLSTM networks. Knowl-Based Syst 252:109409. https://doi.org/10.1016/j.knosys.2022.109409

    Article  Google Scholar 

  36. Woo S, Park J, Lee J Y et al (2018) CBAM: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19. https://doi.org/10.1007/978-3-030-01234-2_1

  37. Fu J, Liu J, Tian H et al (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 3146–3154. https://doi.org/10.1109/CVPR.2019.00326

  38. Zhang P, Li T, Wang G et al (2021) Multi-source information fusion based on rough set theory: A review. Inf Fusion 68:85–117. https://doi.org/10.1016/j.inffus.2020.11.004

    Article  Google Scholar 

  39. Li S, Kang X, Fang L et al (2017) Pixel-level image fusion: A survey of the state of the art. Inf Fusion 33:100–112. https://doi.org/10.1016/j.inffus.2016.05.004

    Article  Google Scholar 

  40. He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  41. Antoni BC, Nuno V (2009) Bayesian Poisson regression for crowd counting. 2009 IEEE 12th international conference on computer vision. IEEE, Kyoto, pp 545–551

  42. Zhang Y, Zhou D, Chen S et al (2016) Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 589–597. https://doi.org/10.1109/CVPR.2016.70

  43. Cao X, Wang Z, Zhao Y et al (2018) Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 734–750. https://doi.org/10.1007/978-3-030-01228-1-45

  44. Li Y, Zhang X, Chen D (2018) CSRNet: Dilated convolutional neural networks for understanding the highly congested scenes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1091–1100. https://doi.org/10.1109/CVPR.2018.00120

  45. Zhang Q, Chan A B (2019) Wide-area crowd counting via ground-plane density maps and multi-view fusion CNNs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 8297–8306. https://doi.org/10.1109/CVPR.2019.00849

  46. Zhang J, Fan D P, Dai Y et al (2020) UC-Net: Uncertainty inspired RGB-D saliency detection via conditional variational autoencoders. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 8582–8591. https://doi.org/10.1109/CVPR42600.2020.00861

  47. Pang Y, Zhang L, Zhao X et al (2020) Hierarchical dynamic filtering network for RGB-D salient object detection. In: European Conference on Computer Vision. Springer, Cham, pp 235–252. https://doi.org/10.1007/978-3-030-58595-2_15

  48. Fan D P, Zhai Y, Borji A et al (2020) BBS-Net: RGB-D salient object detection with a bifurcated backbone strategy network. In: European Conference on Computer Vision. Springer, Cham, pp 275–292. https://doi.org/10.1007/978-3-030-58610-2_17

  49. Liu J, Gao C, Meng D et al (2018) DecideNet: Counting varying density crowds through attention guided detection and density estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5197–5206. https://doi.org/10.1109/CVPR.2018.00545

  50. Idrees H, Tayyab M, Athrey K et al (2018) Composition loss for counting, density map estimation and localization in dense crowds. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 532–546. https://doi.org/10.1007/978-3-030-01216-8-33

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Acknowledgements

This paper was supported by the National Natural Science Foundation of China (No. 62163016, 62066014, 62202165), the Natural Science Foundation of Jiangxi Province (20212ACB202001, 20202BABL202018), and the Double Thousand Plan of Jiangxi Province of China.

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Authors and Affiliations

  1. College of Software Engineering, East China Jiao tong University, Nanchang, 330013, China

    Ying Yu, Zhen Cai, Jin Qian & Hong Tang

  2. Department of Computer Science and Technology, Tongji University, Shanghai, 201804, China

    Duoqian Miao

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Correspondence to Ying Yu.

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Yu, Y., Cai, Z., Miao, D. et al. An interactive network based on transformer for multimodal crowd counting. Appl Intell 53, 22602–22614 (2023). https://doi.org/10.1007/s10489-023-04721-2

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