A fast hybrid computer vision technique for real-time embedded bus passenger flow calculation through camera
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Embedded systems were proven to be adjusted for general purpose passenger detection and could not be easily upgraded to include situations in which passengers use umbrellas in instances of rain or in similar situations.
“Embedded systems were proven to be adjusted for general purpose passenger detection and could not be easily upgraded to include situations in which passengers use umbrellas in instances of rain or in similar situations.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [41]
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,089 · see citing works
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,437 · see citing works
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855 · see citing works
Theory of Reproducing Kernels.
Aronszajn, N. (1950) · Transactions of the American Mathematical Society · doi:10.1090/s0002-9947-1950-0051437-7 · Cited by 6,218 · see citing works
Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.316 · Cited by 4,313 · see citing works
SUN database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539970 · Cited by 3,283 · see citing works
A TUTORIAL ON SUPPORT VECTOR MACHINES FOR PATTERN RECOGNITION, DATA MINING AND KNOWLEDGE DISCOVERY
Burger, C. J. C. (1998) · Cited by 3,261 · see citing works
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., & Brox, T. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.179 · Cited by 3,177 · see citing works
A discriminatively trained, multiscale, deformable part model
Felzenszwalb, P. F., McAllester, D., & Ramanan, D. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587597 · Cited by 2,911 · see citing works
Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing
Vapnik, V. N., Golowich, S. E., & Smola, A. J. (1996) · Cited by 2,776 · see citing works
Detecting Pedestrians Using Patterns of Motion and Appearance
Viola, P. A., Jones, M., & Snow, D. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-6644-8 · Cited by 2,098 · see citing works
Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning
Айзерман, М. А. (1964) · Automation and Remote Control · Cited by 1,478 · see citing works
Articulated pose estimation with flexible mixtures-of-parts
Yang, Y., & Ramanan, D. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995741 · Cited by 999 · see citing works
Pedestrian Detection in Crowded Scenes
Leibe, B., Seemann, E., & Schiele, B. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.272 · Cited by 871 · see citing works
Wu, B., & Nevatia, R. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.74 · Cited by 828 · see citing works
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
Hui, T.-W., Tang, X., & Loy, C. C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00936 · Cited by 817 · see citing works
Learning to associate: HybridBoosted multi-target tracker for crowded scene
Li, Y., Huang, C., & Nevatia, R. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206735 · Cited by 633 · see citing works
Face recognition across pose: A review
Zhang, X., & Gao, Y. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.04.017 · Cited by 486 · see citing works
EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
Zhu, A. Z., Yuan, L., Chaney, K. P., & Daniilidis, K. (2018) · Robotics: Science and Systems · doi:10.15607/rss.2018.xiv.062 · Cited by 478 · see citing works
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance
Morris, B., & Trivedi, M. M. (2008) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2008.927109 · Cited by 475 · see citing works
Counting Crowded Moving Objects
Rabaud, V., & Belongie, S. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.92 · Cited by 417 · see citing works
Unsupervised Bayesian Detection of Independent Motion in Crowds
Brostow, G., & Cipolla, R. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.320 · Cited by 408 · see citing works
Segmentation and Tracking of Multiple Humans in Crowded Environments
Zhao, T., Nevatia, R., & Wu, B. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70770 · Cited by 369 · see citing works
Tracking multiple humans in crowded environment
Zhao, T., & Nevatia, R. (2004) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2004.1315192 · Cited by 340 · see citing works
Estimation of number of people in crowded scenes using perspective transformation
Lin, S., Chen, J.-Y., & Chao, H.-X. (2001) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/3468.983420 · Cited by 331 · see citing works
Bayesian human segmentation in crowded situations
Zhao, T., & Nevatia, R. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211503 · Cited by 297 · see citing works
Skapura, D. M. (1995) · Cited by 267 · see citing works
Marked point processes for crowd counting
Ge, W., & Collins, R. T. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206621 · Cited by 266 · see citing works
A novel method for tracking and counting pedestrians in real-time using a single camera
Masoud, O., & Papanikolopoulos, N. (2001) · IEEE Transactions on Vehicular Technology · doi:10.1109/25.950328 · Cited by 228 · see citing works
Face detection and tracking in video sequences using the modifiedcensus transformation
Küblbeck, C., & Ernst, A. (2006) · Image and Vision Computing · doi:10.1016/j.imavis.2005.08.005 · Cited by 178 · see citing works
Beltran, A., Erickson, V. L., & Cerpa, A. (2013) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/2528282.2528301 · Cited by 158 · see citing works
A camera-based system for tracking people in real time
Segen, J. (1996) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1996.546795 · Cited by 154 · see citing works
Multiple Component Learning for Object Detection
Dollár, P., Babenko, B., Belongie, S., Perona, P., & Zhuowen Tu, A. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88688-4_16 · Cited by 136 · see citing works
Behavioral Priors for Detection and Tracking of Pedestrians in Video Sequences
Antonini, G., Venegas-Martinez, S., Bierlaire, M., & Thiran, J. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-005-4797-0 · Cited by 122 · see citing works
Fusion of feature sets and classifiers for facial expression recognition
Zavaschi, T. H. H., Britto, A. S., Oliveira, L. S., & Koerich, A. L. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.07.074 · Cited by 116 · see citing works
Segmentation of people in motion
Shio, A., & Sklansky, J. (2002) · IEEE Workshop on Motion and Video Computing · doi:10.1109/wvm.1991.212768 · Cited by 105 · see citing works
SPFTN: A Joint Learning Framework for Localizing and Segmenting Objects in Weakly Labeled Videos
Zhang, D., Han, J., Yang, L., & Xu, D. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2881114 · Cited by 105 · see citing works
Robust detection and tracking of human faces with an active camera
Comaniciu, D., & Ramesh, V. (2002) · doi:10.1109/vs.2000.856853 · Cited by 104 · see citing works
An Intelligent People-Flow Counting Method for Passing Through a Gate
Chen, T.-H., Chen, T.-Y., & Chen, Z. (2006) · IEEE Conference on Robotics, Automation and Mechatronics (RAM) · doi:10.1109/ramech.2006.252623 · Cited by 95 · see citing works
Detection and tracking of shopping groups in stores
Haritaoğlu, I., & Flickner, M. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2001.990507 · Cited by 91 · see citing works
Real-Time People Counting Using Multiple Lines
Barandiarán, J., Murguia, B., & Boto, F. (2008) · International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS) · doi:10.1109/wiamis.2008.27 · Cited by 86 · see citing works
Wedding, D. K. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)88328-2 · Cited by 85 · see citing works
Detecting and counting people in surveillance applications
Liu, X., Tu, P., Rittscher, J., Perera, A. G. A., & Krahnstoever, N. O. (2006) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2005.1577286 · Cited by 81 · see citing works
Tracking and counting moving people
Rossi, M., & Bozzoli, A. (2002) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.1994.413857 · Cited by 72 · see citing works
People count system using multi-sensing application
Hashimoto, K., Morinaka, K., Yoshiike, N., Kawaguchi, C., & Matsueda, S. (2002) · International Conference on Solid-State Sensors, Actuators and Microsystems · doi:10.1109/sensor.1997.635472 · Cited by 70 · see citing works
A people counting system based on head-shoulder detection and tracking in surveillance video
Xu, H., Lv, P., & Meng, L. (2010) · International Conference on Computer Design and Applications (ICCDA) · doi:10.1109/iccda.2010.5540833 · Cited by 70 · see citing works
A method of counting the passing people by using the stereo images
Terada, K., Yoshida, D., Oe, S., & Yamaguchi, J. (1999) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.1999.822913 · Cited by 69 · see citing works
2D Principal Component Analysis for Face and Facial-Expression Recognition
Oliveira, L. S., Mansano, M. D. R., Koerich, A. L., & Britto, A. D. S. (2010) · Computing in Science & Engineering · doi:10.1109/mcse.2010.149 · Cited by 61 · see citing works
REAL TIME FACE DETECTION AND TRACKING USING OPENCV
Kalas, M. S. (2014) · Cited by 60 · see citing works
Background subtraction under varying illumination
Matsuyama, T., Wada, T., Habe, H., & Tanahashi, K. (2006) · Systems and Computers in Japan · doi:10.1002/scj.10166 · Cited by 56 · see citing works
A People Counting System Based on Face-Detection
Chen, T.-Y., Chen, C.-H., Wang, D.-J., & Kuo, Y.-L. (2010) · International Conference on Genetic and Evolutionary Computing · doi:10.1109/icgec.2010.178 · Cited by 51 · see citing works
Real-Time Fine Grained Occupancy Estimation Using Depth Sensors on ARM Embedded Platforms
Munir, S., Arora, R. S., Hesling, C., Li, J., Francis, J., Shelton, C., Martin, C., Rowe, A., & Berges, M. E. (2017) · IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) · doi:10.1109/rtas.2017.8 · Cited by 45 · see citing works
PoseFlow: A Deep Motion Representation for Understanding Human Behaviors in Videos
Zhang, D., Guo, G., Huang, D., & Han, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00707 · Cited by 42 · see citing works
A Fast and Robust People Counting Method in Video Surveillance
Zhang, E., & Chen, F. (2007) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/cis.2007.85 · Cited by 39 · see citing works
Robust hierarchical multiple hypothesis tracker for multiple object tracking
Zulkifley, M. A., Moran, B., & Rawlinson, D. (2012) · 2012 19th IEEE International Conference on Image Processing · doi:10.1016/j.eswa.2012.03.004 · Cited by 36 · see citing works
A flexible sequence alignment approach on pattern mining and matching for human activity recognition
Huang, P., Lee, S.-S., Kuo, Y.-H., & Lee, K. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.05.057 · Cited by 33 · see citing works
“Long-Memory” Matching of Interacting Complex Objects from Real Image Sequences
Teseï, A., Teschioni, A., Regazzoni, C. S., & Vernazza, G. L. (1997) · Elsevier eBooks · doi:10.1016/b978-044482307-6/50042-4 · Cited by 33 · see citing works
Li, X., & Wang, Y. (2007) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/cis.2007.19 · Cited by 30 · see citing works
People Count Estimation In Small Crowds
Morerio, P., Marcenaro, L., & Regazzoni, C. S. (2012) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2012.88 · Cited by 26 · see citing works
The man who invented management
Byrne, J., & Gerdes, L. (2006) · IEEE Engineering Management Review · doi:10.1109/emr.2006.1679048 · Cited by 23 · see citing works
Advances in automated pedestrian counting
Sexton, G. (1995) · doi:10.1049/cp:19950479 · Cited by 22 · see citing works
DETECTING PERSONS USING HOUGH CIRCLE TRANSFORM IN SURVEILLANCE VIDEO
Liu, H., Qian, Y., & Lin, S. (2010) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0002856002670270 · Cited by 20 · see citing works
A vision-based method to estimate passenger flow in bus
Yu, H., He, Z., & Liu, J. (2007) · International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) · doi:10.1109/ispacs.2007.4445972 · Cited by 15 · see citing works
A hybrid tracking method for scaled and oriented objects in crowded scenes
Talu, M. F., Türkoğlu, İ., & Cebeci, M. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.04.153 · Cited by 14 · see citing works
Robust people counting in crowded environment
Ye, W., Xu, Y., & Zhong, Z. (2007) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2007.4522323 · Cited by 14 · see citing works
A Video Stabilization Method Based on Inter-frame Image Matching Score
Zheng, Q., & Yang, M. (2017) · Global Journal of Computer Science and Technology · Cited by 12 · see citing works
Conrad, G., & Johnsonbaugh, R. (1994) · ACM Symposium on Applied Computing · doi:10.1145/326619.326649 · Cited by 11 · see citing works
Detection of fence climbing using activity recognition by Support Vector Machine classifier
Kolekar, M. H., Bharti, N., & Patil, P. N. (2016) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2016.7848029 · Cited by 10 · see citing works
A RAM based neural network approach to people counting
Schofield, A. J. (1995) · IEE International Conference on Image Processing and its Applications (IPA) · doi:10.1049/cp:19950740 · Cited by 10 · see citing works
A people counting system for business analytics
Pane, C., Gasparini, M., Prati, A., Gualdi, G., & Cucchiara, R. (2013) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2013.6636629 · Cited by 7 · see citing works
An Area-Based Decision Rule for People-Counting Systems
Park, H. H., Lee, H. G., Noh, S.-I., & Kim, J. (2006) · Lecture notes in computer science · doi:10.1007/11848035_60 · Cited by 6 · see citing works
Performance Evaluation of Multi-camera Visual Tracking
Marcenaro, L., Morerio, P., & Regazzoni, C. S. (2012) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2012.86 · Cited by 6 · see citing works
A New Method for Passenger Flow Counting System Based on Surveillance Video
Yu, M., & We, L. (2009) · International Conference on Intelligent Networks and Intelligent Systems · doi:10.1109/icinis.2009.122 · Cited by 5 · see citing works
Pedestrian detection method of vision based on HOG features
Guo, Z. (2011) · Transducer and Microsystem Technologies · Cited by 4 · see citing works
Cascade features based method for pedestrian detection in street scene
Zhang, M. (2011) · Journal of Computer Applications · Cited by 2 · see citing works
A Method for Counting Multidirection Passer-by by Using Circular Space-Time Image
Terada, K., & Matsubara, K. (2009) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.129.1016 · Cited by 1 · see citing works
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Jevinger, Å., Zhao, C., Persson, J. A., & Davidsson, P. (2023) · Public Transport · doi:10.1007/s12469-023-00334-7 · Cited by 88 · see citing works
Radovan, A., Mršić, L., Đambić, G., & Mihaljević, B. (2024) · Eng · doi:10.3390/eng5040172 · Cited by 24 · see citing works
Video-based automatic people counting for public transport: On-bus versus off-bus deployment
McCarthy, C., Ghaderi, H., Martí, F., Jayaraman, P., & Dia, H. (2025) · Computers in Industry · doi:10.1016/j.compind.2024.104195 · Cited by 14 · see citing works
Duan, C. (2021) · Complexity · doi:10.1155/2021/5973531 · Cited by 7 · see citing works
RetractedBLECE: BLE-Based Crowdedness Estimation Method for Restaurants and Public Facilities
Matsuda, Y., Ueda, K., Taya, E., Suwa, H., & Yasumoto, K. (2023) · International Conference on Mobile Computing and Ubiquitous Network (ICMU) · doi:10.23919/icmu58504.2023.10412158 · Cited by 7 · see citing works
Matsuda, Y., Suwa, H., Hayashi, K., Yoshimura, T., Yoshihara, A., & Arai, I. (2025) · IEICE Transactions on Communications · doi:10.23919/transcom.2025cei0006 · Cited by 3 · see citing works
Janairo, A. G., Concepcion, R., Fernando, A., & Guillermo, M. A. (2026) · Urban Science · doi:10.3390/urbansci10070380 · Cited by 3 · see citing works
Detecting Number of Passengers in a Moving Vehicle with Publicly Available Data
Branco, L. R. F., Qiao, F., & Zhang, Y. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-82196-8_39 · Cited by 2 · see citing works
Bell, D. S., James, P., & López‐García, M. (2023) · Sensors · doi:10.3390/s23249665 · Cited by 1 · see citing works
Программа формирования стека методов искусственного интеллекта при решении прикладных задач
Пучков, А. Ю., A.Yu., P., Лобанева, Е. И., Ивановна, Л. Е., Vasilkova, M. A., & Vasilkova, M. A. (2021) · Международный журнал Программные продукты и системы · doi:10.15827/0236-235x.135.390-398 · Cited by 1 · see citing works
Liu, Y., We, Z., Pan, S., Li, Y., Wang, X., Chen, Z., & Samuel, R. D. J. (2022) · Annals of Operations Research · doi:10.1007/s10479-021-04383-8 · Cited by 1 · see citing works
RetractedInvestigating the Impact of Public Transport Usage Behaviors on Air Pollution After COVID-19
Kuşkapan, E., & Campisi, T. (2025) · The Open Transportation Journal · doi:10.2174/0126671212395722250628150539
- Authors
- Haq, E. U., Huarong, X., Xuhui, C., Wanqing, Z., Jianping, F., & Abid, F.
- Affiliations
- Xiamen University of Technology (CN)
- Northwest University (CN)
- Venue
- Multimedia Tools and Applications
- Published
- 2019-10-14
- Volume
- 79
- Issue
- 1-2
- Pages
- 1007-1036
- ISSN
- 1380-7501 (ISSN-L)
- 1573-7721
- OpenAlex
- W2980434863
- Cited by
- 12
- References
- 79
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 3 · 2022: 1 · 2021: 3
- FWCI
- 0.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Multimedia Tools and Applications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- head detection, head tracking, Kanade-Lucas-Tomasi tracker, histogram of oriented gradients, support vector machine, local binary pattern, embedded vision, public transport, trajectory analysis
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