计算机工程与应用 ›› 2022, Vol. 58 ›› Issue (11): 33-46.DOI: 10.3778/j.issn.1002-8331.2111-0281
卢振坤,刘胜,钟乐,刘绍航,张甜
出版日期:
2022-06-01
发布日期:
2022-06-01
LU Zhenkun, LIU Sheng, ZHONG Le, LIU Shaohang, ZHANG Tian
Online:
2022-06-01
Published:
2022-06-01
摘要: 人群计数广泛应用在公共安防、视频监控和智慧城市建设等领域,对控制特定场所人数、指挥公共交通、防止疫情蔓延、保障社会稳定具有重要积极意义。传统的计数方法精度不高、场景受限,随着深度学习的发展,传统方法逐渐被卷积神经网络(convolutional neural network,CNN)方法代替。介绍了人群计数的研究背景、现状和发展趋势,叙述了两种传统方法;从计数精度、网络结构、评价指标和数据集等方面重点分析了CNN方法,发现CNN技术可以有效解决多尺度和跨场景等问题;阐述了基于Vision Transformer(ViT)序列的弱监督计数方法并且对比各类方法。对未来人群计数的研究前景做出展望。
卢振坤, 刘胜, 钟乐, 刘绍航, 张甜. 人群计数研究综述[J]. 计算机工程与应用, 2022, 58(11): 33-46.
LU Zhenkun, LIU Sheng, ZHONG Le, LIU Shaohang, ZHANG Tian. Survey on Reaserch of Crowd Counting[J]. Computer Engineering and Applications, 2022, 58(11): 33-46.
[1] 邵峰,陈刚,陈珂,等.基于权重哈尔小波的XML包含连接估计方法[J].浙江大学学报(工学版),2009,43(1):28-35. SHAO F,CHEN G,CHEN K,et al.Estimate XML containment join size using weighted Haar wavelet[J].Journal of Zhejiang University(Engineering),2009,43(1):28-35. [2] DALAL N,TRIGGS B.Histograms of oriented gradients for human detection[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005:886-893. [3] 赵超,王腾江,刘士军,等.融合选择提取与子类聚类的快速Shapelet发现算法[J].软件学报,2020,31(3):763-777. ZHAO C,WANG T J,LIU S J et al.Fast Shapelet discovery algorithm combining selective extraction and subclass clutering[J].Journal of Software,2020,31(3):763-777. [4] GAO C,LIU J,FENG Q,et al.People-flow counting in complex environments by combining depth and color information[J].Multimedia Tools & Applications,2016,75(15):9315-9331. [5] VIOLA P.Detecting pedestrians using patterns of motion and appearance[C]//Proceedings of 9th IEEE International Conference on Computer Vision,2003. [6] GALL J,YAO A,RAZAVI N,et al.Hough forests for object detection,tracking,and action recognition[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2011,33(11):2188-2202. [7] PHAM V Q,KOZAKAYA T,YAMAGUCHI O,et al.COUNT forest:CO-Voting uncertain number of targets using random forest for crowd density estimation[C]//2015 IEEE International Conference on Computer Vision,2015. [8] 王强,孙红.基于像素统计和纹理特征的人群密度估计[J].电子科技,2015(7):129-132. WANG Q,SUN H.Crowd density estimation based on pixel and texture[J].Electronic Science and Technology,2015(7):129-132. [9] 张朋,温宏愿.基于混合高斯建模和纹理特征提取的人数统计方法研究[J].价值工程,2018,37(10):235-236. ZHANG P,WEN H Y.A statistical method for the number of people based on hybrid Gaussian modeling and texture feature extraction[J].Value Engineering,2018,37(10):235-236. [10] 王粟,隗磊锋,曾亮.基于GWO-SVM与随机森林的组合光伏功率预测模型[J].昆明理工大学学报(自然科学版),2021,46(5):82-88. WANG S,WEI L F,ZENG L.A combined model for photovoltaic power forecasting based on GMO-SVM and random forest[J].Journal of Kunming University of Science and Technology(Natural Sciences),2021,46(5):82-88. [11] FELZENSZWALB P F,GIRSHICK R B,MCALLESTER D,et al.Object detection with discriminatively trained part-based models[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2010,32(9):1627-1645. [12] 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,2001,31:645-654. [13] WU B,NEVATIA R.Detection and tracking of multiple,partially occluded humans by Bayesian combination of edgelet based part detectors[J].International Journal of Computer Vision,2007,75(2):247-266. [14] MIN L,ZHANG Z,HUANG K,et al.Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection[C]//2008 19th International Conference on Pattern Recognition,2009. [15] RABAUD V,BELONGIE S.Counting crowded moving objects[C]//2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR’06),2006:705-711. [16] 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,2001,31(6):645-654. [17] XU T,CHEN X,WEI G,et al.Crowd counting using accumulated HOG[C]//2016 12th International Conference on Natural Computation,Fuzzy Systems and Knowledge Discovery(ICNC-FSKD),2016:1877-1881. [18] LARADJI I H,ROSTAMZADEH N,PINHEIRO P O,et al.Where are the blobs:counting by localization with point supervision[C]//Proceedings of the European Conference on Computer Vision(ECCV),2018:547-562. [19] LIU Y,SHI M,ZHAO Q,et al.Point in,box out:beyond counting persons in crowds[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2019:6469-6478. [20] CHAN A B,VASCONCELOS N.Bayesian poisson regression for crowd counting[C]//2009 IEEE 12th International Conference on Computer Vision,2009:545-551. [21] RYAN D,DENMAN S,FOOKES C,et al.Crowd counting using multiple local features[C]//2009 Digital Image Computing:Techniques and Applications,2009:81-88. [22] KE C,CHEN C L,GONG S,et al.Feature mining for localised crowd counting[C]//British Machine Vision Conference,2012. [23] PARAGIOS N,RAMESH V.A MRF-based approach for real-time subway monitoring[C]//Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2001. [24] CHAN A B,LIANG Z S J,VASCONCELOS N.Privacy preserving crowd monitoring:counting people without people models or tracking[C]//2008 IEEE Conference on Computer Vision and Pattern Recognition,2008:1-7. [25] MCDONALD G C.Ridge regression[J].Wiley Interdisciplinary Reviews:Computational Statistics,2009,1(1):93-100. [26] MARANA A N,COSTA L F,LOTUFO R A,et al.On the efficacy of texture analysis for crowd monitoring[C]//International Symposium on Computer Graphics,Image Processing,and Vision,1998:354-361. [27] CHO S Y,CHOW T W S,LEUNG C T.A neural-based crowd estimation by hybrid global learning algorithm[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B:Cybernetics,1999,29(4):535-541. [28] KONG D,GRAY D,TAO H.A viewpoint invariant approach for crowd counting[C]//18th International Conference on Pattern Recognition(ICPR’06),2006:1187-1190. [29] KONG D,GRAY D,TAO H.Counting pedestrians in crowds using viewpoint invariant training[C]//British Machine Vision Conference,2005. [30] SAFARI N,TANEM J P,ROSTE T.A block-based predistortion for high power-amplifier linearization[J].IEEE Transactions on Microwave Theory and Techniques,2006,54(6):2813-2820. [31] LI J,HUANG L,LIU C.Robust people counting in video surveillance:dataset and system[C]//2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance(AVSS),2011:54-59. [32] CHAN A B,LIANG Z S,VASCONCELOS N.Privacy preserving crowd monitoring:counting people without people models or tracking[C]//2008 IEEE Conference on Computer Vision and Pattern Recognition,2008:1-7. [33] LIN T Y,LIN Y Y,WENG M F,et al.Cross camera people counting with perspective estimation and occlusion handling[C]//2011 IEEE International Workshop on Information Forensics and Security,2011:1-6. [34] LEMPITSKY V,ZISSERMAN A.Learning to count objects in images[C]//Advances in Neural Information Processing Systems,2010:1324-1332. [35] RODRIGUEZ M,LAPTEV I,SIVIC J,et al.Density-aware person detection and tracking in crowds[C]//2011 International Conference on Computer Vision,2011:2423-2430. [36] WANG C,ZHANG H,YANG L,et al.Deep people counting in extremely dense crowds[C]//Proceedings of the 23rd ACM International Conference on Multimedia,2015:1299-1302. [37] FU M,XU P,LI X,et al.Fast crowd density estimation with convolutional neural networks[J].Engineering Applications of Artificial Intelligence,2015,43:81-88. [38] HAN X B,ZHONG Y F,CAO L Q,et al.Pre-trained Alexnet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification[J].Remote Sensing,2017,9(8):848. [39] ZHANG C,LI H,WANG X,et al.Cross-scene crowd counting via deep convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2015:833-841. [40] TANG G L,LIU Z J,XIONG J.Distinctive image features from illumination and scale invariant keypoints[J].Multimedia Tools and Applications,2019,78(16):23415-23442. [41] BOOMINATHAN L,KRUTHIVENTI S S S,BABU R V,et al.CrowdNet:a deep convolutional network for dense crowd counting[C]//Proceedings of the 24th ACM International Conference on Multimedia.New York:ACM,2016:640-644. [42] CHENG S H,ZHANG G C,LI S.Handwritten digit recognition based on improved VGG16 network[C]//International Conference on Graphic and Image Processing,2019. [43] ZHANG Y Y,ZHOU D S,CHEN S Q,et al.Single-image crowd counting via multi-column convolutional neural network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2016:589-597. [44] XU X,MA Y,SUN W,et al.Exploiting raw images for real-scene super-resolution[J].arXiv:2102.01579,2021. [45] 彭超,王平安,张平.基于加权平均的机载雷达天线随机振动激励条件分解与应用[J].机械与电子,2021,39(7):28-32. PENG C,WANG P A,ZHANG P.Design and verification of a composing space-borne antenna reflector under mechanical environment[J].Machinery and Electronics,2021,39(7):28-32. [46] SAM D B,SURYA S,BABU R V,et al.Switching convolutional neural network for crowd counting[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Honolulu.Piscataway,NJ:IEEE,2017:4031-4039. [47] 朱海龙.复杂气象条件下动态人群场景分析方法研究[D].哈尔滨:哈尔滨工业大学,2012. ZHU H L.Research crowd scene analysis under complicated weather condition[D].Harbin:Harbin Institute of Technology,2012. [48] CHENG Z Q,LI J X,DAI Q,et al.Improving the learning of multi-column convolutional neural network for crowd counting[C]//Proceedings of the 27th ACM International Conference on Multimedia,2019:1897-1906. [49] LI Y,ZHANG X,CHEN D.Csrnet:dilated convolutional neural networks for understanding the highly congested scenes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2018:1091-1100. [50] HUANG S Y,LI X,ZHANG Z F,et al.Body structure aware deep crowd counting[J].IEEE Transactions on Image Processing,2017,27(3):1049-1059. [51] RANJAN V,LE H,HOAI M.Iterative crowd counting[C]//Proceedings of the European Conference on Computer Vision(ECCV),2018:270-285. [52] CHEN Z,CHENG J,YUAN Y,et al.Deep density-aware count regressor[J].arXiv:1908.03314,2019. [53] DEB D,VENTURA J.An aggregated multicolumn dilated convolution network for perspective-free counting[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2018:195-204. [54] LIU M,JIANG J,GUO Z,et al.Crowd counting with fully convolutional neural network[C]//2018 25th IEEE International Conference on Image Processing(ICIP),2018:953-957. [55] WANG Z,XIAO Z,XIE K,et al.In defense of single-column networks for crowd counting[J].arXiv:1808. 06133,2018. [56] DAI F,LIU H,MA Y,et al.Dense scale network for crowd counting[C]//Proceedings of the 2021 International Conference on Multimedia Retrieval,2021:64-72. [57] KANG D,CHAN A.Crowd counting by adaptively fusing predictions from an image pyramid[J].arXiv:1805. 06115,2018. [58] GAO J,WANG Q,LI X.PCC net:perspective crowd counting via spatial convolutional network[J].IEEE Transactions on Circuits and Systems for Video Technology,2019,30(10):3486-3498. [59] ZENG L,XU X,CAI B,et al.Multi-scale convolutional neural networks for crowd counting[C]//2017 IEEE International Conference on Image Processing(ICIP),2017:465-469. [60] ONORO-RUBIO D,LóPEZ-SASTRE R J.Towards perspective-free object counting with deep learning[C]//European Conference on Computer Vision.Cham:Springer,2016:615-629. [61] MOTTAGHI R,CHEN X,LIU X,et al.The role of context for object detection and semantic segmentation in the wild[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2014:891-898. [62] ZHAO H,SHI J,QI X,et al.Pyramid scene parsing network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2017:2881-2890. [63] ZHAO R,OUYANG W,LI H,et al.Saliency detection by multi-context deep learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2015:1265-1274. [64] SINDAGI V A,PATEL V M.Generating high-quality crowd density maps using contextual pyramid CNNs[C]//Proceedings of the IEEE Internation Conference on Computer Vision.Piscataway,NJ:IEEE,2017:1861-1870. [65] 郝晓亮,杨倩倩,夏殷锋,等.基于上下文特征重聚合网络的人群计数[J].信息技术与网络安全,2021,40(7):59-65. HAO X L,YANG Q Q,XIA Y F,et al.Context-aware feature reaggregation network for crowd counting[J].Information Technology and Network Security,2021,40(7):59-65. [66] SHANG C,AI H,BAI B.End-to-end crowd counting via joint learning local and global count[C]//2016 IEEE International Conference on Image Processing(ICIP),2016:1215-1219. [67] LIU W,SALZMANN M,FUA P.Context-aware crowd counting[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2019:5099-5108. [68] CHEN J C,KUMAR A,RANJAN R,et al.A cascaded convolutional neural network for age estimation of unconstrained faces[C]//2016 IEEE 8th International Conference on Biometrics Theory,Applications and Systems(BTAS),2016. [69] DAI J,HE K,SUN J.Instance-aware semantic segmentation via multi-task network cascades[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016:3150-3158. [70] RANJAN R,PATEL V M,CHELLAPPA R.HyperFace:a deep multi-task learning framework for face detection,landmark localization,pose estimation,and gender recognition[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2019,41(1):121-135. [71] SINDAGI V A,PATEL V M.CNN-based cascaded multi-task learning of high-level prior and density estimation for crowd counting[C]//2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance(AVSS),2017:1-6. [72] LIU J,GAO C,MENG D,et al.Decidenet:counting varying density crowds through attention guided detection and density estimation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2018:5197-5206. [73] SHEN Z,XU Y,NI B,et al.Crowd counting via adversarial cross-scale consistency pursuit[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2018:5245-5254. [74] IDREES H,TAYYAB M,ATHREY K,et al.Composition loss for counting,density map estimation and localization in dense crowds[C]//Proceedings of the European Conference on Computer Vision(ECCV),2018:532-546. [75] ZHAO M,ZHANG J,ZHANG C,et al.Leveraging heterogeneous auxiliary tasks to assist crowd counting[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2019:12736-12745. [76] SHI Z,ZHANG L,SUN Y,et al.Multiscale multitask deep NetVLAD for crowd counting[J].IEEE Transactions on Industrial Informatics,2018,14(11):4953-4962. [77] ZHANG L,SHI Z,CHENG M M,et al.Nonlinear regression via deep negative correlation learning[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2021,43(3):982-998. [78] HOSSAIN M,HOSSEINZADEH M,CHANDA O,et al.Crowd counting using scale-aware attention networks[C]//2019 IEEE Winter Conference on Applications of Computer Vision(WACV),2019:1280-1288. [79] CHEN L C,YI Y,JIANG W,et al.Attention to scale:scale-aware semantic image segmentation[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR),2016. [80] LIU N,LONG Y,ZOU C,et al.ADCrowdNet:an attention-injective deformable convolutional network for crowd understanding[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR),2019. [81] JIANG X,ZHANG L,XU M,et al.Attention scaling for crowd counting[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR),2020. [82] VARIOR R R,SHUAI B,TIGHE J,et al.Multi-scale attention network for crowd counting[J].arXiv:1901. 06026,2019. [83] GAO J,WANG Q,YUAN Y.SCAR:spatial-/channel-wise attention regression networks for crowd counting[J].Neurocomputing,2019,363:1-8. [84] ZHU L,ZHAO Z,LU C,et al.Dual path multi-scale fusion networks with attention for crowd counting[J].arXiv:1902.01115,2019. [85] ZOU Z,CHENG Y,QU X,et al.Attend to count:crowd counting with adaptive capacity multi-scale CNNs[J].Neurocomputing,2019,367:75-83. [86] LIANG D,CHEN X,XU W,et al.TransCrowd:weakly-supervised crowd counting with transformer[J].arXiv:2104.09116,2021. [87] CHAN A B,LIANG Z S J,VASCONCELOS N.Privacy preserving crowd monitoring:counting people without people models or tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2008:1-7. [88] IDREES H,SALEEMI I,SEIBERT C,et al.Multi-source multi-scale counting in extremely dense crowd images[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2013:2547-2554. [89] CAO X K,WANG Z P,ZHAO Y Y,et al.Scale aggregation network for accurate and efficient crowd counting[C]//LNCS 11209:Proceedings of the 15th European Conference on Computer Vision.Berlin:Springer,2018:734-750. [90] SAM D B,SAJJAN N N,BABU R V,et al.Divide and grow:capturing huge diversity in crowd images with incrementally growing CNN[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2018:3618-3626. [91] ZHANG L,SHI M J,CHEN Q B.Crowd counting via scale-adaptive convolutional neural network[C]//Proceedings of the IEEE Winter Conference on Applications of Computer Vision.Piscataway,NJ:IEEE,2018:1113-1121. [92] AICH S,STAVNESS I.Global sum pooling:a generalization trick for object counting with small datasets of large images[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.Piscataway,NJ:IEEE,2019:73-82. [93] ZHANG A,YUE L,SHEN J,et al.Attentional neural fields for crowd counting[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision,2019:5714-5723. [94] YAN Z,YUAN Y,ZUO W,et al.Perspective-guided convolution networks for crowd counting[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision,2019:952-961. [95] MA Z,WEI X,HONG X,et al.Bayesian loss for crowd count estimation with point supervision[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision,2019:6142-6151. [96] LIU L,LU H,ZOU H,et al.Weighing counts:sequential crowd counting by reinforcement learning[C]//European Conference on Computer Vision.Springer,Cham,2020:164-181. [97] WAN J,CHAN A.Modeling noisy annotations for crowd counting[C]//Advances in Neural Information Processing Systems,2020. [98] WANG B,LIU H,SAMARAS D,et al.Distribution matching for crowd counting[J].arXiv:2009.13077,2020. [99] YANG Y,LI G,WU Z,et al.Weakly-supervised crowd counting learns from sorting rather than locations[C]//Computer Vision-ECCV 2020:16th European Conference,Glasgow,UK,August 23-28,2020:1-17. [100] BAI S,HE Z Q,QIAO Y,et al.Adaptive dilated network with-self-correction supervision for counting[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2020:4593-4602. [101] SONG Q,WANG C,JIANG Z,et al.Rethinking counting and localization in crowds:a purely point-based framework[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision,2021:3365-3374. [102] LEI Y,LIU Y,ZHANG P,et al.Towards using count-level weak supervision for crowd counting[J].Pattern Recognition,2021,109:107616. [103] SUN G,LIU Y,PROBST T,et al.Boosting crowd counting with transformers[J].arXiv:2105.10926,2021. |
[1] | 张昊雨, 张德. 基于图结构的级联注意力视觉问答模型[J]. 计算机工程与应用, 2023, 59(6): 155-161. |
[2] | 彭佩, 张美玲, 郑东. 融合CNN_LSTM的侧信道攻击[J]. 计算机工程与应用, 2023, 59(6): 268-276. |
[3] | 杨春霞, 马文文, 陈启岗, 桂强. 融合CNN-SAM与GAT的多标签文本分类模型[J]. 计算机工程与应用, 2023, 59(5): 106-114. |
[4] | 秦晴, 王卫星, 刘清华, 蒙德庆. 基于骨架信息的民族舞蹈典型动作识别[J]. 计算机工程与应用, 2023, 59(5): 281-288. |
[5] | 杨宇航, 林敏, 王长缨, 钟一文. 融合频率域特征的双路网络模型诊断新冠肺炎[J]. 计算机工程与应用, 2023, 59(5): 321-327. |
[6] | 杜昱峥, 曹慧, 聂永琦, 魏德健, 冯妍妍. 深度学习在阿尔茨海默病分类诊断中的应用[J]. 计算机工程与应用, 2023, 59(3): 49-65. |
[7] | 钟智鹏, 王海龙, 苏贵斌, 柳林, 裴冬梅. 融合CNN-BiLSTM和自注意力模型的音乐情感识别[J]. 计算机工程与应用, 2023, 59(3): 94-103. |
[8] | 高红霞, 郜伟. 融合关键点属性与注意力表征的人脸表情识别[J]. 计算机工程与应用, 2023, 59(3): 118-126. |
[9] | 孙韩玉, 黄丽霞, 张雪英, 李娟. 基于双通道卷积门控循环网络的语音情感识别[J]. 计算机工程与应用, 2023, 59(2): 170-177. |
[10] | 苏庆, 林佳锐, 黄海滨, 黄剑锋. 融合MAML和CBAM的安卓恶意应用家族分类模型[J]. 计算机工程与应用, 2023, 59(2): 271-279. |
[11] | 李翔, 张涛, 张哲, 魏宏杨, 钱育蓉. Transformer在计算机视觉领域的研究综述[J]. 计算机工程与应用, 2023, 59(1): 1-14. |
[12] | 朱宇斌, 李文根, 关佶红, 张毅超. 一种面向人群计数的卷积注意力网络模型[J]. 计算机工程与应用, 2023, 59(1): 156-161. |
[13] | 刘钊, 杨帆, 司亚中. 时域非填充网络视频行为识别算法研究[J]. 计算机工程与应用, 2023, 59(1): 162-168. |
[14] | 张海涛, 苏琳. 结合知识图谱的变分自编码器零样本图像识别[J]. 计算机工程与应用, 2023, 59(1): 236-243. |
[15] | 张鑫, 姚庆安, 赵健, 金镇君, 冯云丛. 全卷积神经网络图像语义分割方法综述[J]. 计算机工程与应用, 2022, 58(8): 45-57. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||