Optics and Precision Engineering, Volume. 30, Issue 10, 1228(2022)

A multivariate information aggregation method for crowd density estimation and counting

Guanghui LIU1、*, Qinmeng WANG1, Xuanrun CHEN1,2, and Yuebo MENG1
Author Affiliations
  • 1School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an70055, China
  • 2Zhongke Xingtu Spatial Data Technology Co., Ltd., Xi'an710199, China
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    References(30)

    [1] X LI, M CHEN, F NIE et al. A multiview-based parameterfree framework for group detection, 4147-4153(2017).

    [2] S F LIN, J Y CHEN, H X CHAO. Estimation of number of people in crowded scenes using perspective transformation. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 31, 645-654(2001).

    [3] T ZHAO, R NEVATIA, B WU. Segmentation and tracking of multiple humans in crowded environments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 1198-1211(2008).

    [4] P VIOLA, M J JONES, D SNOW. Detecting pedestrians using patterns of motion and appearance. International Journal of Computer Vision, 63, 153-161(2005).

    [5] P KILAMBI, E RIBNICK, A J JOSHI et al. Estimating pedestrian counts in groups. Computer Vision and Image Understanding, 110, 43-59(2008).

    [6] [6] 6左静, 巴玉林. 基于多尺度融合的深度人群计数算法[J]. 激光与光电子学进展, 2020, 57(24): 241502. doi: 10.3788/lop57.241502ZUOJ, BAY L. Population-depth counting algorithm based on multiscale fusion[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241502.(in Chinese). doi: 10.3788/lop57.241502

    [7] [7] 7赵建敏, 李雪冬, 李宝山. 基于无人机图像的羊群密集计数算法研究[J]. 激光与光电子学进展, 2021, 58(22): 2210013.ZHAOJ M, LIX D, LIB S. Algorithm of sheep dense counting based on unmanned aerial vehicle images[J]. Laser & Optoelectronics Progress, 2021, 58(22): 2210013.(in Chinese)

    [8] H IDREES, M TAYYAB, K ATHREY et al. Composition loss for counting, density map estimation and localization in dense crowds, 532-546(2018).

    [9] M RODRIGUEZ, I LAPTEV, J SIVIC et al. Density-aware person detection and tracking in crowds, 2423-2430(2011).

    [10] [10] 10慕晓冬, 白坤, 尤轩昂, 等. 基于对比学习方法的遥感影像特征提取与分类[J]. 光学 精密工程, 2021, 29(9): 2222-2234. doi: 10.37188/OPE.20212909.2222MUX D, BAIK, YOUX A, et al. Remote sensing image feature extraction and classification based on contrastive learning method[J]. Opt. Precision Eng., 2021, 29(9): 2222-2234.(in Chinese). doi: 10.37188/OPE.20212909.2222

    [11] [11] 11周涛, 霍兵强, 陆惠玲, 等. 融合多尺度图像的密集神经网络肺部肿瘤识别算法[J]. 光学 精密工程, 2021, 29(7): 1695-1708. doi: 10.37188/OPE.20212907.1695ZHOUT, HUOB Q, LUH L, et al. Lung tumor image recognition algorithm with densenet fusion multi-scale images[J]. Opt. Precision Eng., 2021, 29(7): 1695-1708.(in Chinese). doi: 10.37188/OPE.20212907.1695

    [12] S Q REN, K M HE, R GIRSHICK et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

    [13] [13] 13常亮, 邓小明, 周明全, 等. 图像理解中的卷积神经网络[J]. 自动化学报, 2016, 42(9): 1300-1312. doi: 10.16383/j.aas.2016.c150800CHANGL, DENGX M, ZHOUM Q, et al. Convolutional neural networks in image understanding[J]. Acta Automatica Sinica, 2016, 42(9): 1300-1312.(in Chinese). doi: 10.16383/j.aas.2016.c150800

    [14] C WANG, H ZHANG, L YANG et al. Deep people counting in extremely dense crowds, 1299-1302(2015).

    [15] C ZHANG, H S LI, X G WANG et al. Cross-scene crowd counting via deep convolutional neural networks, 833-841(2015).

    [16] Y Y ZHANG, D S ZHOU, S Q CHEN et al. Single-image crowd counting via multi-column convolutional neural network, 589-597(2016).

    [17] D B SAM, S SURYA, R V BABU. Switching convolutional neural network for crowd counting, 4031-4039(2017).

    [18] L K ZENG, X M XU, B L CAI et al. Multi-scale convolutional neural networks for crowd counting, 465-469(2017).

    [19] Y H LI, X F ZHANG, D M CHEN. CSRNet: dilated convolutional neural networks for understanding the highly congested scenes, 1091-1100(2018).

    [20] V A SINDAGI, V M PATEL. CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting, 1-6(2017).

    [21] W Z LIU, M SALZMANN. Context-aware crowd counting, 5094-5103(2019).

    [22] [22] 22张宇倩, 李国辉, 雷军, 等. FF-CAM: 基于通道注意机制前后端融合的人群计数[J]. 计算机学报, 2021, 44(2): 304-317. doi: 10.11897/SP.J.1016.2021.00304ZHANGY Q, LIG H, LEIJ, et al. FF-CAM: crowd counting based on frontend-backend fusion through channel-attention mechanism[J]. Chinese Journal of Computers, 2021, 44(2): 304-317.(in Chinese). doi: 10.11897/SP.J.1016.2021.00304

    [23] [23] 23孟月波, 陈宣润, 刘光辉, 等. 高低密度多维视角多元信息融合人群计数方法[J/OL]. 控制与决策:1-10[2022-01-16].DOI:10.13195/j.kzyjc. 2021.0520.MENGY B, CHENX R, LIUG H, et al. High and low density multi-dimension perspective multivariate information fusion crowd counting method[J/OL]. Control and Decision: 1-10[2022-01-16].DOI:10.13195/j.kzyjc.2021.0520.(in Chinese)

    [24] Z L ZHANG, X Y ZHANG, C PENG et al. ExFuse Enhancing Feature Fusion for Semantic Segmentation, 269-284(2018).

    [25] L C CHEN, G PAPANDREOU, I KOKKINOS et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).

    [26] M H OH, P OLSEN, K N RAMAMURTHY. Crowd counting with decomposed uncertainty. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 11799-11806(2020).

    [27] I HAROON, T MUHMMAD, A KISHAN et al. Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds, 532-546(2018).

    [28] Q WANG, J Y GAO, W LIN et al. Learning from synthetic data for crowd counting in the wild, 8190-8199(2019).

    [29] Q WANG, J Y GAO, W LIN et al. NWPU-crowd: a large-scale benchmark for crowd counting and localization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 2141-2149(2021).

    [30] Z H MA, X WEI, X P HONG et al. Bayesian loss for crowd count estimation with point supervision, 6141-6150(2019).

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    Guanghui LIU, Qinmeng WANG, Xuanrun CHEN, Yuebo MENG. A multivariate information aggregation method for crowd density estimation and counting[J]. Optics and Precision Engineering, 2022, 30(10): 1228

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    Paper Information

    Category: Information Sciences

    Received: Jan. 19, 2022

    Accepted: --

    Published Online: Jun. 1, 2022

    The Author Email: LIU Guanghui (guanghuil@163.com)

    DOI:10.37188/OPE.20223010.1228

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