16.7.2.4 Vehicle Counting

Chapter Contents (Back)
Vehicle Counting. Counting.
See also Counting Instances, Counting Objects.

Bottesch, H.W.[H. Werner], Freas, D.A.[David A.],
Passive vehicle presence detection system,
US_Patent5,166,681, 07/30/1990.
HTML Version. Simple detector of something there. BibRef 9007

Pang, C.C.C., Lam, W.W.L., Yung, N.H.C.,
A Method for Vehicle Count in the Presence of Multiple-Vehicle Occlusions in Traffic Images,
ITS(8), No. 3, September 2007, pp. 441-459.
IEEE DOI 0710
BibRef

Hu, S.R., Wang, C.M.,
Vehicle Detector Deployment Strategies for the Estimation of Network Origin-Destination Demands Using Partial Link Traffic Counts,
ITS(9), No. 2, June 2008, pp. 288-300.
IEEE DOI 0806
BibRef

Unzueta, L., Nieto, M., Cortes, A., Barandiaran, J., Otaegui, O., Sanchez, P.,
Adaptive Multicue Background Subtraction for Robust Vehicle Counting and Classification,
ITS(13), No. 2, June 2012, pp. 527-540.
IEEE DOI 1206
BibRef

Somasundaram, G., Sivalingam, R., Morellas, V., Papanikolopoulos, N.,
Classification and Counting of Composite Objects in Traffic Scenes Using Global and Local Image Analysis,
ITS(14), No. 1, March 2013, pp. 69-81.
IEEE DOI 1303
BibRef

Zhao, R., Wang, X.,
Counting Vehicles from Semantic Regions,
ITS(14), No. 2, 2013, pp. 1016-1022.
IEEE DOI Clustering algorithms; Semantics; Tracking; Semantic region; vehicle counting 1307
BibRef

Taghvaeeyan, S., Rajamani, R.,
Portable Roadside Sensors for Vehicle Counting, Classification, and Speed Measurement,
ITS(15), No. 1, February 2014, pp. 73-83.
IEEE DOI 1403
Global Positioning System BibRef

Wang, R.[Rui], Zhang, L.[Lei], Xiao, K.J.[Ke-Jiang], Sun, R.L.[Rong-Li], Cui, L.[Li],
EasiSee: Real-Time Vehicle Classification and Counting via Low-Cost Collaborative Sensing,
ITS(15), No. 1, February 2014, pp. 414-424.
IEEE DOI 1403
cameras BibRef

Moranduzzo, T., Melgani, F.,
Automatic Car Counting Method for Unmanned Aerial Vehicle Images,
GeoRS(52), No. 3, March 2014, pp. 1635-1647.
IEEE DOI 1403
automobiles BibRef

Liang, M.[Mingpei], Huang, X.Y.[Xin-Yu], Chen, C.H.[Chung-Hao], Chen, X.[Xin], Tokuta, A.[Alade],
Counting and Classification of Highway Vehicles by Regression Analysis,
ITS(16), No. 5, October 2015, pp. 2878-2888.
IEEE DOI 1511
image classification BibRef

Salvadori, C.[Claudio], Petracca, M.[Matteo], Bocchino, S.[Stefano], Pelliccia, R.[Riccardo], Pagano, P.[Paolo],
A low-cost vehicle counter for next-generation ITS,
RealTimeIP(10), No. 4, December 2015, pp. 741-757.
Springer DOI 1512
BibRef

Kamkar, S., Safabakhsh, R.,
Vehicle detection, counting and classification in various conditions,
IET-ITS(10), No. 6, 2016, pp. 406-413.
DOI Link 1608
feature extraction BibRef

Irhebhude, M.E.[Martins E.], Nawahda, A.[Amin], Edirisinghe, E.A.[Eran A.],
View Invariant Vehicle Type Recognition and Counting System using Multiple Features,
IJCVSP(6), No. 1, 2016, pp. 3.
PDF File. 1612
BibRef

Zhang, Y.S.[Yun-Sheng], Zhao, C.[Chihang], Zhang, Q.[Qiuge],
Counting vehicles in urban traffic scenes using foreground time-spatial images,
IET-ITS(11), No. 2, March 2017, pp. 61-67.
DOI Link 1703
BibRef

Xu, H.X.[Hai-Xia], Zhou, W.[Wei], Zhu, J.[Jiang], Huang, X.[Xia], Wang, W.[Wei],
Vehicle counting based on double virtual lines,
SIViP(11), No. 5, July 2017, pp. 905-912.
WWW Link. 1706
BibRef

Yang, H.H.[Hong-Hong], Qu, S.[Shiru],
Real-time vehicle detection and counting in complex traffic scenes using background subtraction model with low-rank decomposition,
IET-ITS(12), No. 1, February 2018, pp. 75-85.
DOI Link 1801
BibRef

Balid, W., Tafish, H., Refai, H.H.,
Intelligent Vehicle Counting and Classification Sensor for Real-Time Traffic Surveillance,
ITS(19), No. 6, June 2018, pp. 1784-1794.
IEEE DOI 1806
Algorithm design and analysis, Estimation, Magnetometers, Real-time systems, Reliability, Surveillance, Vehicle detection, vehicles detection and classification BibRef

Gao, Z., Zhai, R., Wang, P., Yan, X., Qin, H., Tang, Y., Ramesh, B.,
Synergizing Appearance and Motion With Low Rank Representation for Vehicle Counting and Traffic Flow Analysis,
ITS(19), No. 8, August 2018, pp. 2675-2685.
IEEE DOI 1808
Spatiotemporal phenomena, Vehicle detection, Robustness, Lighting, Cameras, Matrix decomposition, Training, Appearance and motion, traffic flow BibRef

Chen, L.[Lili], Zhang, Z.[Zhengdao], Peng, L.[Li],
Fast single shot multibox detector and its application on vehicle counting system,
IET-ITS(12), No. 10, December 2018, pp. 1406-1413.
DOI Link 1812
BibRef

Wang, Z.L.[Zi-Lei], Liu, X.[Xu], Feng, J.S.[Jia-Shi], Yang, J.[Jian], Xi, H.S.[Hong-Sheng],
Compressed-Domain Highway Vehicle Counting by Spatial and Temporal Regression,
CirSysVideo(29), No. 1, January 2019, pp. 263-274.
IEEE DOI 1901
BibRef
Earlier: A2, A1, A3, A5, Only:
Highway Vehicle Counting in Compressed Domain,
CVPR16(3016-3024)
IEEE DOI 1612
Videos, Feature extraction, Road transportation, Estimation, Surveillance, Metadata, Algorithm design and analysis, compressed domain BibRef

Jiang, T.[Tao], Cai, M.D.[Ming-Dai], Zhang, Y.L.[Yu-Long], Jia, X.J.[Xiao-Jie],
Fast video-based queue length detection approach for self-organising traffic control,
IET-ITS(13), No. 4, April 2019, pp. 670-676.
DOI Link 1903
BibRef

Czyzewski, A.[Andrzej], Kotus, J.[Józef], Szwoch, G.[Grzegorz],
Estimating Traffic Intensity Employing Passive Acoustic Radar and Enhanced Microwave Doppler Radar Sensor,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link 2001
BibRef

Li, S.[Shuang], Chang, F.L.[Fa-Liang], Liu, C.S.[Chun-Sheng], Li, N.J.[Nan-Jun],
Vehicle counting and traffic flow parameter estimation for dense traffic scenes,
IET-ITS(14), No. 12, December 2020, pp. 1517-1523.
DOI Link 2011
BibRef

Li, S.[Shuang], Chang, F.[Faliang], Liu, C.S.[Chun-Sheng],
Bi-Directional Dense Traffic Counting Based on Spatio-Temporal Counting Feature and Counting-LSTM Network,
ITS(22), No. 12, December 2021, pp. 7395-7407.
IEEE DOI 2112
Feature extraction, Bidirectional control, Estimation, Vehicle detection, Solid modeling, Real-time systems, counting Long Short-Term Memory (cLSTM) BibRef

Liu, C.H.[Cheng-Huan], Huynh, D.Q.[Du Q.], Sun, Y.C.[Yu-Chao], Reynolds, M.[Mark], Atkinson, S.[Steve],
A Vision-Based Pipeline for Vehicle Counting, Speed Estimation, and Classification,
ITS(22), No. 12, December 2021, pp. 7547-7560.
IEEE DOI 2112
Cameras, Pipelines, Detectors, Videos, Australia, Urban areas, Intelligent transportation systems (ITS), camera calibration BibRef

Liu, L.[Liang], Cao, Z.G.[Zhi-Guo], Lu, H.[Hao], Xiong, H.P.[Hai-Peng], Shen, C.H.[Chun-Hua],
NSSNet: Scale-Aware Object Counting With Non-Scale Suppression,
ITS(23), No. 4, April 2022, pp. 3103-3114.
IEEE DOI 2204
Feature extraction, Transportation, Estimation, Fuses, Monitoring, Task analysis, Object counting, intelligent transportation, multi-scale models BibRef

Yu, X.Y.[Xiao-Yuan], Liang, Y.Y.[Yan-Yan], Lin, X.X.[Xu-Xin], Wan, J.[Jun], Wang, T.[Tian], Dai, H.N.[Hong-Ning],
Frequency Feature Pyramid Network With Global-Local Consistency Loss for Crowd-and-Vehicle Counting in Congested Scenes,
ITS(23), No. 7, July 2022, pp. 9654-9664.
IEEE DOI 2207
Feature extraction, Data mining, Convolution, Task analysis, Frequency-domain analysis, Correlation, Kernel, Context prediction, global-local consistency loss BibRef

Xu, H.H.[Hong-Hui], Cai, Z.P.[Zhi-Peng], Li, R.[Ruinian], Li, W.[Wei],
Efficient CityCam-to-Edge Cooperative Learning for Vehicle Counting in ITS,
ITS(23), No. 9, September 2022, pp. 16600-16611.
IEEE DOI 2209
Urban areas, Cameras, Videos, Feature extraction, Kernel, Servers, Magnetic sensors, CityCam-to-Edge, vehicle counting, lightweight scheme BibRef

Meng, Y.[Yanda], Bridge, J.[Joshua], Zhao, Y.T.[Yi-Tian], Joddrell, M.[Martha], Qiao, Y.H.[Yi-Hong], Yang, X.Y.[Xiao-Yun], Huang, X.W.[Xiao-Wei], Zheng, Y.L.[Ya-Lin],
Transportation Object Counting With Graph-Based Adaptive Auxiliary Learning,
ITS(24), No. 3, March 2023, pp. 3422-3437.
IEEE DOI 2303
Task analysis, Adaptation models, Representation learning, Feature extraction, Cognition, Transportation, Training, adaptive auxiliary task BibRef

Liao, L.[Liang], Xiao, J.[Jing], Yang, Y.[Yan], Ma, X.[Xujie], Wang, Z.[Zheng], Satoh, S.[Shin'ichi],
High temporal frequency vehicle counting from low-resolution satellite images,
PandRS(198), 2023, pp. 45-59.
Elsevier DOI 2304
Vehicle counting, Satellite images, Temporal frequency observation, Spatial consistency, Location consistency BibRef

Cao, Q.X.[Qian-Xia], Shan, Z.Y.[Zhen-Yu], Long, K.[Kejun], Wang, Z.W.[Zheng-Wu],
GhostCount: A lightweight convolution network based on high-altitude video for vehicle instantaneous counting in dense traffic scenes,
IET-ITS(17), No. 5, 2023, pp. 943-959.
DOI Link 2305
computer vision, convolutional neural nets, edge detection, intelligent transportation systems, road traffic BibRef

Zhao, B.[Bin], Han, P.F.[Peng-Fei], Li, X.L.[Xue-Long],
Vehicle Perception From Satellite,
PAMI(46), No. 4, April 2024, pp. 2545-2554.
IEEE DOI 2403
Satellites, Videos, Task analysis, Object detection, Surveillance, Traffic control, Deep learning, Density estimation, remote sensing, vehicle counting BibRef


Majin, J.J., Valencia, Y.M., Stivanello, M.E., Stemmer, M.R., Salazar, J.D.,
A Novel Deep Learning Based Method for Detection and Counting Of Vehicles In Urban Traffic Surveillance Systems,
ISPRS21(B2-2021: 793-800).
DOI Link 2201
BibRef

Dobeš, P.[Petr], Španhel, J.[Jakub], Bartl, V.[Vojtech], Juránek, R.[Roman], Herout, A.[Adam],
Density-Based Vehicle Counting with Unsupervised Scale Selection,
DICTA20(1-8)
IEEE DOI 2201
Training, Annotations, Predictive models, Robustness, Task analysis, Standards, Drones, Object counting, Traffic surveillance, Deep learning BibRef

Ranjan, V.[Viresh], Sharma, U.[Udbhav], Nguyen, T.[Thu], Hoai, M.[Minh],
Learning To Count Everything,
CVPR21(3393-3402)
IEEE DOI 2111

WWW Link. Code, Counting. Visualization, Codes, Annotations, Animals, Detectors, Pattern recognition BibRef

Ha, S.V.U.[Synh Viet-Uyen], Chung, N.M.[Nhat Minh], Nguyen, T.C.[Tien-Cuong], Phan, H.N.[Hung Ngoc],
Tiny-PIRATE: A Tiny model with Parallelized Intelligence for Real-time Analysis as a Traffic countEr,
AICity21(4114-4123)
IEEE DOI 2109
Road transportation, Tracking, Image edge detection, Computational modeling, Urban areas, Detectors, Real-time systems BibRef

Kocur, V.[Viktor], Ftácnik, M.[Milan],
Multi-Class Multi-Movement Vehicle Counting Based on CenterTrack,
AICity21(4004-4010)
IEEE DOI 2109
Tracking, Urban areas, Neural networks, Object detection, Computer architecture BibRef

Rangnekar, A.[Aneesh], Yao, Y.[Yi], Hoffman, M.[Matthew], Divakaran, A.[Ajay],
Fine-tuning for One-look Regression Vehicle Counting in Low-shot Aerial Datasets,
WAAMI20(5-18).
Springer DOI 2103
BibRef

Huang, J., Ding, G., Guo, Y., Yang, D., Wang, S., Wang, T., Zhang, Y.,
Drone-Based Car Counting via Density Map Learning,
VCIP20(239-242)
IEEE DOI 2102
Gaussian processes, learning (artificial intelligence), drone-based car counting, drone-based image BibRef

Bui, K.N., Yi, H., Cho, J.,
A Vehicle Counts by Class Framework using Distinguished Regions Tracking at Multiple Intersections,
City20(2466-2474)
IEEE DOI 2008
Tracking, Object detection, Monitoring, Feature extraction, Vehicle detection, Trajectory, Artificial intelligence BibRef

Abdelhalim, A., Abbas, M.,
Towards Real-time Traffic Movement Count and Trajectory Reconstruction Using Virtual Traffic Lanes,
City20(2527-2533)
IEEE DOI 2008
Trajectory, Cameras, Video sequences, Tracking, Graphical user interfaces, Real-time systems, Artificial intelligence BibRef

Lu, J.C.[Jin-Cheng], Xia, M.[Meng], Gao, X.[Xu], Yang, X.P.[Xi-Peng], Tao, T.R.[Tian-Ran], Meng, H.[Hao], Zhang, W.[Wei], Tan, X.[Xiao], Shi, Y.F.[Yi-Feng], Li, G.B.[Guan-Bin], Ding, E.[Errui],
Robust and Online Vehicle Counting at Crowded Intersections,
AICity21(3997-4003)
IEEE DOI 2109
Tracking, Detectors, Pattern recognition BibRef

Liu, Z.J.[Zhong-Ji], Zhang, W.[Wei], Gao, X.[Xu], Meng, H.[Hao], Tan, X.[Xiao], Zhu, X.X.[Xiao-Xing], Xue, Z.[Zhan], Ye, X.Q.[Xiao-Qing], Zhang, H.W.[Hong-Wu], Wen, S.L.[Shi-Lei], Ding, E.[Errui],
Robust Movement-Specific Vehicle Counting at Crowded Intersections,
City20(2617-2625)
IEEE DOI 2008
Feature extraction, Trajectory, Object tracking, Object detection, Kalman filters, Task analysis BibRef

Darji, D., Vejarano, G.,
Counting Static Targets Using an Unmanned Aerial Vehicle On-the-Fly and Autonomously,
CRV18(206-213)
IEEE DOI 1812
Cameras, Automobiles, Unmanned aerial vehicles, Trajectory, Roads, Portable computers, Target tracking, unmanned aerial vehicle, target counting BibRef

Bouaich, S., Mahraz, M.A., Riffi, J., Tairi, H.,
Vehicle counting system in real-time,
ISCV18(1-4)
IEEE DOI 1807
image motion analysis, nearest neighbour methods, object detection, road traffic, road vehicles, virtual line BibRef

Zhang, S., Wu, G., Costeira, J.P., Moura, J.M.F.,
FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras,
ICCV17(3687-3696)
IEEE DOI 1802
convolution, image resolution, learning (artificial intelligence), neural nets, Visualization BibRef

Hsieh, M.R., Lin, Y.L., Hsu, W.H.,
Drone-Based Object Counting by Spatially Regularized Regional Proposal Network,
ICCV17(4165-4173)
IEEE DOI 1802
SLAM (robots), automobiles, autonomous aerial vehicles, cameras, mobile robots, object detection, robot vision, LPNs, Videos BibRef

Zhu, J.L.[Jian-Liang], Ai, Y.F.[Yun-Feng], Tian, B.[Bin],
Real-time vehicle queue estimation of large-scale traffic scene,
ICIVC17(1160-1165)
IEEE DOI 1708
Algorithm design and analysis, Estimation, Head, Safety, background learning, high-speed toll station, vehicle queue estimation BibRef

Katsuki, T.[Takayuki], Morimura, T.[Tetsuro], Idé, T.[Tsuyoshi],
Unsupervised object counting without object recognition,
ICPR16(3627-3632)
IEEE DOI 1705
Cameras, Indexes, Object recognition, Pattern recognition, Training, Training, data BibRef

Lessard, A., Belisle, F., Bilodeau, G.A.[Guillaume-Alexandre], Saunier, N.,
The Counting App, or How to Count Vehicles in 500 Hours of Video,
Traffic16(1592-1600)
IEEE DOI 1612
BibRef

Sakla, W.A.[Wesam A.], Konjevod, G.[Goran], Mundhenk, T.N.[T. Nathan],
Deep Multi-modal Vehicle Detection in Aerial ISR Imagery,
WACV17(916-923)
IEEE DOI 1609
Image color analysis, Object detection, Proposals, Spatial resolution, Vehicle, detection BibRef

Mundhenk, T.N.[T. Nathan], Konjevod, G.[Goran], Sakla, W.A.[Wesam A.], Boakye, K.[Kofi],
A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning,
ECCV16(III: 785-800).
Springer DOI 1611
BibRef

Quesada, J., Rodriguez, P.[Paul],
Automatic Vehicle Counting Method Based on Principal Component Pursuit Background Modeling,
ICIP16(3822-3826)
IEEE DOI 1610
Computer vision BibRef

Tourani, A., Shahbahrami, A.,
Vehicle counting method based on digital image processing algorithms,
IPRIA15(1-6)
IEEE DOI 1603
Kalman filters BibRef

Shirazi, M.S.[Mohammad Shokrolah], Morris, B.[Brendan],
Vision-Based Vehicle Counting with High Accuracy for Highways with Perspective View,
ISVC15(II: 809-818).
Springer DOI 1601
BibRef

Wang, W.[Wei], Gee, T.[Tim], Price, J.[Jeff], Qi, H.R.[Hai-Rong],
Real Time Multi-vehicle Tracking and Counting at Intersections from a Fisheye Camera,
WACV15(17-24)
IEEE DOI 1503
Cameras; Real-time systems; Space vehicles; Target tracking; Trajectory BibRef

Miller, N.[Nicholas], Thomas, M.A.[Mohan A.], Eichel, J.A.[Justin A.], Mishra, A.[Akshaya],
A Hidden Markov Model for Vehicle Detection and Counting,
CRV15(269-276)
IEEE DOI 1507
Detectors BibRef

Oñoro-Rubio, D.[Daniel], López-Sastre, R.J.[Roberto J.],
Towards Perspective-Free Object Counting with Deep Learning,
ECCV16(VII: 615-629).
Springer DOI 1611
BibRef

Guerrero-Gómez-Olmedo, R.[Ricardo], Torre-Jiménez, B.[Beatriz], López-Sastre, R.J.[Roberto J.], Maldonado-Bascón, S.[Saturnino], Oñoro-Rubio, D.[Daniel],
Extremely Overlapping Vehicle Counting,
IbPRIA15(423-431).
Springer DOI 1506
BibRef

Tamersoy, B.[Birgi], Aggarwal, J.K.,
Counting Vehicles in Highway Surveillance Videos,
ICPR10(3631-3635).
IEEE DOI 1008
BibRef
Earlier:
Robust Vehicle Detection for Tracking in Highway Surveillance Videos Using Unsupervised Learning,
AVSBS09(529-534).
IEEE DOI 0909
BibRef

Hinz, S.[Stefan], Weihing, D.[Diana], Suchandt, S.[Steffen], Bamler, R.[Richard],
Detection and velocity estimation of moving vehicles in high-resolution spaceborne synthetic aperture radar data,
OTCBVS08(1-6).
IEEE DOI 0806
BibRef

Meyer, F.[Franz], Hinz, S.[Stefan], Laika, A.[Andreas], Suchandt, S.[Steffen], Bamler, R.[Richard],
Performance Analysis of Spaceborne SAR Vehicle Detection and Velocity Estimation,
PCV06(xx-yy).
PDF File. 0609
BibRef

Hinz, S.,
Detection and counting of cars in aerial images,
ICIP03(III: 997-1000).
IEEE DOI 0312
BibRef

Chiu, M.Y.[Ming-Yee], Depommier, R., Spindler, T.,
An embedded real-time vision system for 24-hour indoor/outdoor car-counting applications,
ICPR04(III: 338-341).
IEEE DOI 0409
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Vehicle Tracking, Vehicle Motion Analysis .


Last update:Mar 16, 2024 at 20:36:19