Optics and Precision Engineering, Volume. 30, Issue 10, 1228(2022)
A multivariate information aggregation method for crowd density estimation and counting
[1] LI X, CHEN M, NIE F et al. A multiview-based parameterfree framework for group detection[C], 4147-4153(2017).
[2] LIN S F, CHEN J Y, CHAO H X. Estimation of number of people in crowded scenes using perspective transformation[J]. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 31, 645-654(2001).
[3] ZHAO T, NEVATIA R, WU B. Segmentation and tracking of multiple humans in crowded environments[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 1198-1211(2008).
[4] VIOLA P, JONES M J, SNOW D. Detecting pedestrians using patterns of motion and appearance[J]. International Journal of Computer Vision, 63, 153-161(2005).
[5] KILAMBI P, RIBNICK E, JOSHI A J et al. Estimating pedestrian counts in groups[J]. 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] IDREES H, TAYYAB M, ATHREY K et al. Composition loss for counting, density map estimation and localization in dense crowds[C], 532-546(2018).
[9] RODRIGUEZ M, LAPTEV I, SIVIC J et al. Density-aware person detection and tracking in crowds[C], 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] REN S Q, HE K M, GIRSHICK R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. 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] WANG C, ZHANG H, YANG L et al. Deep people counting in extremely dense crowds[C], 1299-1302(2015).
[15] ZHANG C, LI H S, WANG X G et al. Cross-scene crowd counting via deep convolutional neural networks[C], 833-841(2015).
[16] ZHANG Y Y, ZHOU D S, CHEN S Q et al. Single-image crowd counting via multi-column convolutional neural network[C], 589-597(2016).
[17] SAM D B, SURYA S, BABU R V. Switching convolutional neural network for crowd counting[C], 4031-4039(2017).
[18] ZENG L K, XU X M, CAI B L et al. Multi-scale convolutional neural networks for crowd counting[C], 465-469(2017).
[19] LI Y H, ZHANG X F, CHEN D M. CSRNet: dilated convolutional neural networks for understanding the highly congested scenes[C], 1091-1100(2018).
[20] SINDAGI V A, PATEL V M. CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting[C], 1-6(2017).
[21] LIU W Z, SALZMANN M. Context-aware crowd counting[C], 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] ZHANG Z L, ZHANG X Y, PENG C et al.
[25] CHEN L C, PAPANDREOU G, KOKKINOS I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).
[26] OH M H, OLSEN P, RAMAMURTHY K N. Crowd counting with decomposed uncertainty[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 11799-11806(2020).
[27] HAROON I, MUHMMAD T, KISHAN A et al. Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds[C], 532-546(2018).
[28] WANG Q, GAO J Y, LIN W et al. Learning from synthetic data for crowd counting in the wild[C], 8190-8199(2019).
[29] WANG Q, GAO J Y, LIN W et al. NWPU-crowd: a large-scale benchmark for crowd counting and localization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 2141-2149(2021).
[30] MA Z H, WEI X, HONG X P et al. Bayesian loss for crowd count estimation with point supervision[C], 6141-6150(2019).
Get Citation
Copy Citation Text
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
Category: Information Sciences
Received: Jan. 19, 2022
Accepted: --
Published Online: Jun. 1, 2022
The Author Email: Guanghui LIU (guanghuil@163.com)