Artykuły w czasopismach na temat „Dense crowd”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Sprawdź 50 najlepszych artykułów w czasopismach naukowych na temat „Dense crowd”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Przeglądaj artykuły w czasopismach z różnych dziedzin i twórz odpowiednie bibliografie.
Xue, Yiran, Peng Liu, Ye Tao i Xianglong Tang. "Abnormal Prediction of Dense Crowd Videos by a Purpose–Driven Lattice Boltzmann Model". International Journal of Applied Mathematics and Computer Science 27, nr 1 (28.03.2017): 181–94. http://dx.doi.org/10.1515/amcs-2017-0013.
Pełny tekst źródłaSam, Deepak Babu, Neeraj N. Sajjan, Himanshu Maurya i R. Venkatesh Babu. "Almost Unsupervised Learning for Dense Crowd Counting". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 8868–75. http://dx.doi.org/10.1609/aaai.v33i01.33018868.
Pełny tekst źródłaZhang, Jin, Sheng Chen, Sen Tian, Wenan Gong, Guoshan Cai i Ying Wang. "A Crowd Counting Framework Combining with Crowd Location". Journal of Advanced Transportation 2021 (17.02.2021): 1–14. http://dx.doi.org/10.1155/2021/6664281.
Pełny tekst źródłaMa, Junjie, Yaping Dai i Kaoru Hirota. "A Survey of Video-Based Crowd Anomaly Detection in Dense Scenes". Journal of Advanced Computational Intelligence and Intelligent Informatics 21, nr 2 (15.03.2017): 235–46. http://dx.doi.org/10.20965/jaciii.2017.p0235.
Pełny tekst źródłaMiao, Yunqi, Zijia Lin, Guiguang Ding i Jungong Han. "Shallow Feature Based Dense Attention Network for Crowd Counting". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 07 (3.04.2020): 11765–72. http://dx.doi.org/10.1609/aaai.v34i07.6848.
Pełny tekst źródłaHuang, Liangjun, Shihui Shen, Luning Zhu, Qingxuan Shi i Jianwei Zhang. "Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting". Sensors 22, nr 9 (22.04.2022): 3233. http://dx.doi.org/10.3390/s22093233.
Pełny tekst źródłaNarain, Rahul, Abhinav Golas, Sean Curtis i Ming C. Lin. "Aggregate dynamics for dense crowd simulation". ACM Transactions on Graphics 28, nr 5 (grudzień 2009): 1–8. http://dx.doi.org/10.1145/1618452.1618468.
Pełny tekst źródłaKok, Ven Jyn, i Chee Seng Chan. "Granular-based dense crowd density estimation". Multimedia Tools and Applications 77, nr 15 (5.12.2017): 20227–46. http://dx.doi.org/10.1007/s11042-017-5418-y.
Pełny tekst źródłaZhang, Jin, Luqin Ye, Jiajia Wu, Dan Sun i Cheng Wu. "A Fusion-Based Dense Crowd Counting Method for Multi-Imaging Systems". International Journal of Intelligent Systems 2023 (18.10.2023): 1–13. http://dx.doi.org/10.1155/2023/6677622.
Pełny tekst źródłaAziz, Muhammad Waqar, Farhan Naeem, Muhammad Hamad Alizai i Khan Bahadar Khan. "Automated Solutions for Crowd Size Estimation". Social Science Computer Review 36, nr 5 (11.09.2017): 610–31. http://dx.doi.org/10.1177/0894439317726510.
Pełny tekst źródłaIlyas, Naveed, Boreom Lee i Kiseon Kim. "HADF-Crowd: A Hierarchical Attention-Based Dense Feature Extraction Network for Single-Image Crowd Counting". Sensors 21, nr 10 (17.05.2021): 3483. http://dx.doi.org/10.3390/s21103483.
Pełny tekst źródłaZhang, Wei, Yongjie Wang, Yanyan Liu i Jianghua Zhu. "Deep convolution network for dense crowd counting". IET Image Processing 14, nr 4 (27.03.2020): 621–27. http://dx.doi.org/10.1049/iet-ipr.2019.0435.
Pełny tekst źródłaHan, Suyu. "Hybrid Attention Fusion in Dense Crowd Counting". Frontiers in Computing and Intelligent Systems 2, nr 1 (23.11.2022): 35–38. http://dx.doi.org/10.54097/fcis.v2i1.2707.
Pełny tekst źródłaDang, Huu-Tu, Benoit Gaudou i Nicolas Verstaevel. "A literature review of dense crowd simulation". Simulation Modelling Practice and Theory 134 (lipiec 2024): 102955. http://dx.doi.org/10.1016/j.simpat.2024.102955.
Pełny tekst źródłaKleinmeier, Benedikt, Gerta Köster i John Drury. "Agent-based simulation of collective cooperation: from experiment to model". Journal of The Royal Society Interface 17, nr 171 (październik 2020): 20200396. http://dx.doi.org/10.1098/rsif.2020.0396.
Pełny tekst źródłaZeng, Hui, Rong Hu, Xiaohui Huang i Zhiying Peng. "Robot Navigation in Crowd Based on Dual Social Attention Deep Reinforcement Learning". Mathematical Problems in Engineering 2021 (24.09.2021): 1–11. http://dx.doi.org/10.1155/2021/7114981.
Pełny tekst źródłaQiu, Xiangfeng, Jin Ye, Siyu Chen i Jinhe Su. "Hierarchical Inverse Distance Transformer for Enhanced Localization in Dense Crowds". Electronics 13, nr 12 (11.06.2024): 2289. http://dx.doi.org/10.3390/electronics13122289.
Pełny tekst źródłaLi, Pengfei, Min Zhang, Jian Wan i Ming Jiang. "Multiscale Aggregate Networks with Dense Connections for Crowd Counting". Computational Intelligence and Neuroscience 2021 (11.11.2021): 1–12. http://dx.doi.org/10.1155/2021/9996232.
Pełny tekst źródłaGong, Shengrong, Shan Zhong i Ran Yan. "Crowd counting via scale-adaptive convolutional neural network in extremely dense crowd images". International Journal of Computer Applications in Technology 61, nr 4 (2019): 318. http://dx.doi.org/10.1504/ijcat.2019.10024872.
Pełny tekst źródłaYan, Ran, Shengrong Gong i Shan Zhong. "Crowd counting via scale-adaptive convolutional neural network in extremely dense crowd images". International Journal of Computer Applications in Technology 61, nr 4 (2019): 318. http://dx.doi.org/10.1504/ijcat.2019.103298.
Pełny tekst źródłaMa, Zhiheng, Xing Wei, Xiaopeng Hong, Hui Lin, Yunfeng Qiu i Yihong Gong. "Learning to Count via Unbalanced Optimal Transport". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 3 (18.05.2021): 2319–27. http://dx.doi.org/10.1609/aaai.v35i3.16332.
Pełny tekst źródłaFagette, Antoine, Nicolas Courty, Daniel Racoceanu i Jean-Yves Dufour. "Unsupervised dense crowd detection by multiscale texture analysis". Pattern Recognition Letters 44 (lipiec 2014): 126–33. http://dx.doi.org/10.1016/j.patrec.2013.09.020.
Pełny tekst źródłaEshel, Ran, i Yael Moses. "Tracking in a Dense Crowd Using Multiple Cameras". International Journal of Computer Vision 88, nr 1 (17.11.2009): 129–43. http://dx.doi.org/10.1007/s11263-009-0307-0.
Pełny tekst źródłaMeng, Xiaolong. "SENetCount: An Optimized Encoder-Decoder Architecture with Squeeze-and-Excitation for Crowd Counting". Wireless Communications and Mobile Computing 2022 (20.06.2022): 1–13. http://dx.doi.org/10.1155/2022/2964683.
Pełny tekst źródłaSato, Yuta, Yoko Sasaki i Hiroshi Takemura. "STP4: spatio temporal path planning based on pedestrian trajectory prediction in dense crowds". PeerJ Computer Science 9 (30.10.2023): e1641. http://dx.doi.org/10.7717/peerj-cs.1641.
Pełny tekst źródłaWong, Vivian W. H., i Kincho H. Law. "Fusion of CCTV Video and Spatial Information for Automated Crowd Congestion Monitoring in Public Urban Spaces". Algorithms 16, nr 3 (10.03.2023): 154. http://dx.doi.org/10.3390/a16030154.
Pełny tekst źródłaZhang, Yulin, i Zhengyong Feng. "Crowd-Aware Mobile Robot Navigation Based on Improved Decentralized Structured RNN via Deep Reinforcement Learning". Sensors 23, nr 4 (6.02.2023): 1810. http://dx.doi.org/10.3390/s23041810.
Pełny tekst źródłaLiu, Yan-Bo, Rui-Sheng Jia, Jin-Tao Yu, Ruo-Nan Yin i Hong-Mei Sun. "Crowd density estimation via a multichannel dense grouping network". Neurocomputing 449 (sierpień 2021): 61–70. http://dx.doi.org/10.1016/j.neucom.2021.03.078.
Pełny tekst źródłaMetivet, T., L. Pastorello i P. Peyla. "How to push one's way through a dense crowd". EPL (Europhysics Letters) 121, nr 5 (1.03.2018): 54003. http://dx.doi.org/10.1209/0295-5075/121/54003.
Pełny tekst źródłaZhang, Yanhao, Qingming Huang, Lei Qin, Sicheng Zhao, Hongxun Yao i Pengfei Xu. "Representing dense crowd patterns using bag of trajectory graphs". Signal, Image and Video Processing 8, S1 (22.07.2014): 173–81. http://dx.doi.org/10.1007/s11760-014-0669-9.
Pełny tekst źródłaZeng, Xin, Yunpeng Wu, Shizhe Hu, Ruobin Wang i Yangdong Ye. "DSPNet: Deep scale purifier network for dense crowd counting". Expert Systems with Applications 141 (marzec 2020): 112977. http://dx.doi.org/10.1016/j.eswa.2019.112977.
Pełny tekst źródłaChaudhry, Huma, Mohd Shafry Mohd Rahim, Tanzila Saba i Amjad Rehman. "Crowd region detection in outdoor scenes using color spaces". International Journal of Modeling, Simulation, and Scientific Computing 09, nr 02 (20.03.2018): 1850012. http://dx.doi.org/10.1142/s1793962318500125.
Pełny tekst źródłaXu, Han, Xiangxia Ren, Weiguo Song, Jun Zhang i Rayyan Saidahmed. "Spatial and temporal analysis of the bottleneck flow under different walking states with a moving obstacle". Journal of Statistical Mechanics: Theory and Experiment 2023, nr 1 (1.01.2023): 013401. http://dx.doi.org/10.1088/1742-5468/aca2a2.
Pełny tekst źródłaHan, Kang, Wanggen Wan, Haiyan Yao i Li Hou. "Image Crowd Counting Using Convolutional Neural Network and Markov Random Field". Journal of Advanced Computational Intelligence and Intelligent Informatics 21, nr 4 (20.07.2017): 632–38. http://dx.doi.org/10.20965/jaciii.2017.p0632.
Pełny tekst źródłaKhan, Khalil, Rehan Ullah Khan, Waleed Albattah, Durre Nayab, Ali Mustafa Qamar, Shabana Habib i Muhammad Islam. "Crowd Counting Using End-to-End Semantic Image Segmentation". Electronics 10, nr 11 (28.05.2021): 1293. http://dx.doi.org/10.3390/electronics10111293.
Pełny tekst źródłaNuhu, Aliyu Shuaibu, Aamir Saeed i Ibrahima Faye. "A COMPARATIVE ANALYSIS OF TECHNIQUES FOR CROWD BEHAVIOUR DETECTION IN DENSE SCENES". Platform : A Journal of Science and Technology 4, nr 2 (30.11.2021): 32. http://dx.doi.org/10.61762/pjstvol4iss2art12757.
Pełny tekst źródłaXiang, Jun, i Na Liu. "Crowd Density Estimation Method Using Deep Learning for Passenger Flow Detection System in Exhibition Center". Scientific Programming 2022 (18.02.2022): 1–9. http://dx.doi.org/10.1155/2022/1990951.
Pełny tekst źródłada Silva, Felipe Tavares, Halane Maria Braga Fernandes Brito i Roberto Leal Pimentel. "Modeling of crowd load in vertical direction using biodynamic model for pedestrians crossing footbridges". Canadian Journal of Civil Engineering 40, nr 12 (grudzień 2013): 1196–204. http://dx.doi.org/10.1139/cjce-2011-0587.
Pełny tekst źródłaZhu, Aichun, Guoxiu Duan, Xiaomei Zhu, Lu Zhao, Yaoying Huang, Gang Hua i Hichem Snoussi. "CDADNet: Context-guided dense attentional dilated network for crowd counting". Signal Processing: Image Communication 98 (październik 2021): 116379. http://dx.doi.org/10.1016/j.image.2021.116379.
Pełny tekst źródłaHuang, Liangjun, Luning Zhu, Shihui Shen, Qing Zhang i Jianwei Zhang. "SRNet: Scale-Aware Representation Learning Network for Dense Crowd Counting". IEEE Access 9 (2021): 136032–44. http://dx.doi.org/10.1109/access.2021.3115963.
Pełny tekst źródłaShami, Mamoona Birkhez, Salman Maqbool, Hasan Sajid, Yasar Ayaz i Sen-Ching Samson Cheung. "People Counting in Dense Crowd Images Using Sparse Head Detections". IEEE Transactions on Circuits and Systems for Video Technology 29, nr 9 (wrzesień 2019): 2627–36. http://dx.doi.org/10.1109/tcsvt.2018.2803115.
Pełny tekst źródłaHu, Yaocong, Huan Chang, Fudong Nian, Yan Wang i Teng Li. "Dense crowd counting from still images with convolutional neural networks". Journal of Visual Communication and Image Representation 38 (lipiec 2016): 530–39. http://dx.doi.org/10.1016/j.jvcir.2016.03.021.
Pełny tekst źródła钟, 德军. "Dense Crowd Counting Algorithm Based on Dual-Branch Self-Attention". Journal of Image and Signal Processing 13, nr 02 (2024): 130–37. http://dx.doi.org/10.12677/jisp.2024.132012.
Pełny tekst źródłaLiu, Bangquan, Zhen Liu, Dechao Sun i Chunyue Bi. "An Evacuation Route Model of Crowd Based on Emotion and Geodesic". Mathematical Problems in Engineering 2018 (1.10.2018): 1–10. http://dx.doi.org/10.1155/2018/5397071.
Pełny tekst źródłaFan, Jiwei, Xiaogang Yang, Ruitao Lu, Xueli Xie i Weipeng Li. "Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention". Drones 5, nr 3 (27.07.2021): 68. http://dx.doi.org/10.3390/drones5030068.
Pełny tekst źródłaZhu, Rui, Kangning Yin, Hang Xiong, Hailian Tang i Guangqiang Yin. "Masked Face Detection Algorithm in the Dense Crowd Based on Federated Learning". Wireless Communications and Mobile Computing 2021 (4.10.2021): 1–8. http://dx.doi.org/10.1155/2021/8586016.
Pełny tekst źródłaZhang, Guoli. "Crowd Counting Based on Context-Aware and Multi Scale Feature Fusion". Frontiers in Computing and Intelligent Systems 2, nr 2 (26.12.2022): 12–15. http://dx.doi.org/10.54097/fcis.v2i2.3736.
Pełny tekst źródłaRadhan, Ali Raza, Fareed Ahmed Jokhio, Ghulam Hussain, Kamran Javed i Arsalan Ahmed. "Multi-Scale Pooling In Deep Neural Networks For Dense Crowd Estimation". Sukkur IBA Journal of Emerging Technologies 5, nr 1 (30.06.2022): 54–63. http://dx.doi.org/10.30537/sjet.v5i1.1023.
Pełny tekst źródłaTao, Huiqiang. "Statistical Calculation of Dense Crowd Flow Antiobscuring Method considering Video Continuity". Mathematical Problems in Engineering 2022 (31.03.2022): 1–9. http://dx.doi.org/10.1155/2022/6185986.
Pełny tekst źródłaSheng, Biyun, Chunhua Shen, Guosheng Lin, Jun Li, Wankou Yang i Changyin Sun. "Crowd Counting via Weighted VLAD on a Dense Attribute Feature Map". IEEE Transactions on Circuits and Systems for Video Technology 28, nr 8 (sierpień 2018): 1788–97. http://dx.doi.org/10.1109/tcsvt.2016.2637379.
Pełny tekst źródła