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research-article

A survey of advances in vision-based vehicle re-identification

Authors:

Sultan Daud Khan,

Habib UllahAuthors Info & Claims

Volume 182, Issue C

Pages 50 - 63

https://doi.org/10.1016/j.cviu.2019.03.001

Published: 01 May 2019 Publication History

Abstract

Vehicle re-identification (V-reID) has become significantly popular in the community due to its applications and research significance. In particular, the V-reID is an important problem that still faces numerous open challenges. This paper reviews different V-reID methods including sensor based methods, hybrid methods, and vision based methods which are further categorized into hand-crafted feature based methods and deep feature based methods. The vision based methods make the V-reID problem particularly interesting, and our review systematically addresses and evaluates these methods for the first time. We conduct experiments on four comprehensive benchmark datasets and compare the performances of recent hand-crafted feature based methods and deep feature based methods. We present the detail analysis of these methods in terms of mean average precision (mAP) and cumulative matching curve (CMC). These analyses provide objective insight into the strengths and weaknesses of these methods. We also provide the details of different V-reID datasets and critically discuss the challenges and future trends of V-reID methods.

Highlights

•

For the first time, we systematically review sensor and vision based methods for vehicle re-identification (V-reID).

•

We conduct comprehensive experiments and compare the performances of recent vision based methods.

•

We critically discuss the challenges and future trends of V-reID methods.

References

[1]

Alessandri A., Bolla R., Repetto M., Estimation of freeway traffic variables using information from mobile phones, in: American Control Conference, 2003. Proceedings of the 2003, Vol. 5, IEEE, 2003, pp. 4089–4094.

[2]

Ali S.S.M., George B., Vanajakshi L., Multiple inductive loop detectors for intelligent transportation systems applications: Ramp metering, vehicle re-identification and lane change monitoring systems, in: Computers & Informatics (ISCI), 2013 IEEE Symposium on, IEEE, 2013, pp. 176–180.

[3]

Anagnostopoulos C.N.E., Anagnostopoulos I.E., Psoroulas I.D., Loumos V., Kayafas E., License plate recognition from still images and video sequences: A survey, IEEE Trans. Intell. Transp. Syst. 9 (3) (2008) 377–391.

Digital Library

[4]

Arr G., Sun C., Ramachandran R., Fusion of wavelet transform and color information features for automatic vehicle reidentification in intelligent transportation systems, in: Acoustics, Speech, and Signal Processing, 2004. Proceedings.(ICASSP’04). IEEE International Conference on, Vol. 5, IEEE, 2004, pp. V–285.

[5]

Astarita V., Florian M., The use of mobile phones in traffic management and control, in: Intelligent Transportation Systems, 2001. Proceedings. 2001 IEEE, IEEE, 2001, pp. 10–15.

[6]

Atalar A., A physical model for acoustic signatures, J. Appl. Phys. 50 (12) (1979) 8237–8239.

[7]

Bai Y., Lou Y., Gao F., Wang S., Wu Y., Duan L., Group sensitive triplet embedding for vehicle re-identification, IEEE Trans. Multimed. (2018).

[8]

Balke, K.N., Ullman, G., McCasland, W., Mountain, C., Dudek, C., 1995. Benefits of real-time travel information in Houston, Texas. Technical Report.

[9]

Bar-Gera H., Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: A case study from israel, Transp. Res. C 15 (6) (2007) 380–391.

[10]

Bhaskar A., Chung E., Dumont A.G., Estimation of travel time on urban networks with midlink sources and sinks, Transp. Res. Rec. J. Transp. Res. Board (2121) (2009) 41–54.

[11]

Bhaskar, A., Chung, E., de Mouzon, O., Dumont, A.G., 2008. Analytical modeling and sensitivity analysis for travel time estimation on signalized urban networks.

[12]

Bromley J., Guyon I., LeCun Y., Säckinger E., Shah R., Signature verification using a “siamese” time delay neural network, in: Advances in Neural Information Processing Systems, 1994, pp. 737–744.

[13]

Byeong-Seo Y., Kang S.P., Park C.H., Travel time estimation using mobile data, in: Proceedings of Eastern Asia Society for Transportation Studies, Vol. 5, Citeseer, 2005, pp. 1533–1547.

[14]

Carlson N.A., Berarducci M.P., Federated kalman filter simulation results, Navigation 41 (3) (1994) 297–322.

[15]

Caruso M.J., Withanawasam L.S., Vehicle detection and compass applications using amr magnetic sensors, in: Sensors Expo Proceedings, Vol. 477, 1999, p. 39.

[16]

Charbonnier S., Pitton A.C., Vassilev A., Vehicle re-identification with a single magnetic sensor, in: Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International, IEEE, 2012, pp. 380–385.

[17]

Chen P., Bai X., Liu W., Vehicle color recognition on urban road by feature context, IEEE Trans. Intell. Transp. Syst. 15 (5) (2014) 2340–2346.

[18]

Cheng P., Qiu Z., Ran B., Particle filter based traffic state estimation using cell phone network data, in: Intelligent Transportation Systems Conference, 2006. ITSC’06. IEEE, IEEE, 2006, pp. 1047–1052.

[19]

Cheu R.L., Lee D.H., Xie C., An arterial speed estimation model fusing data from stationary and mobile sensors, in: Intelligent Transportation Systems, 2001. Proceedings. 2001 IEEE, IEEE, 2001, pp. 573–578.

[20]

Cho H., Seo Y.W., Kumar B.V., Rajkumar R.R., A multi-sensor fusion system for moving object detection and tracking in urban driving environments, in: Robotics and Automation (ICRA), 2014 IEEE International Conference on, IEEE, 2014, pp. 1836–1843.

[21]

Choi K., Chung Y., A data fusion algorithm for estimating link travel time, ITS J. 7 (3–4) (2002) 235–260.

[22]

Chopra S., Hadsell R., LeCun Y., Learning a similarity metric discriminatively, with application to face verification, in: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, Vol. 1, IEEE, 2005, pp. 539–546.

[23]

Christiansen I., Hauer L.E., Probing for travel time: Norway applies avi and wim technologies for section probe data, Traffic Technol. Int. (1996).

[24]

CONTAIN T.A., Real-time travel time estimates using media access control address matching, ITE J. (2008).

[25]

Cui C., Sang N., Gao C., Zou L., Vehicle re-identification by fusing multiple deep neural networks, in: Image Processing Theory, Tools and Applications (IPTA), 2017 Seventh International Conference on, IEEE, 2017, pp. 1–6.

[26]

Dailey D.J., Travel-time estimation using cross-correlation techniques, Transp. Res. B 27 (2) (1993) 97–107.

[27]

Dailey, D.J., Harn, P., Lin, P.J., 1996. ITS data fusion. Technical Report. Washington State Department of Transportation.

[28]

Davies P., Vehicle detection and classification, Inf. Technol. Appl. Transp. (1986) 11–40.

[29]

Deng G., Zhou H., Yu G., Yan Z., Hu Y., Xu X., Scalable and parameterized dynamic time warping architecture for efficient vehicle re-identification, in: Transportation Information and Safety (ICTIS), 2017 4th International Conference on, IEEE, 2017, pp. 48–53.

[30]

de Dios OrtAozar J., Willumsen L.G., Modelling Transport, John Wiley Sons, 2011.

[31]

El Faouzi N.E., Data-driven aggregative schemes for multisource estimation fusion: A road travel time application, in: Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2004, Vol. 5434, International Society for Optics and Photonics, 2004, pp. 351–360.

[32]

El Faouzi N.E., Lefevre E., Classifiers and distance-based evidential fusion for road travel time estimation, in: Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006, Vol. 6242, International Society for Optics and Photonics, 2006, p. 62420A.

[33]

Ferencz A., Learned-Miller E.G., Malik J., Building a classification cascade for visual identification from one example, in: Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, Vol. 1, IEEE, 2005, pp. 286–293.

[34]

Feris R.S., Siddiquie B., Petterson J., Zhai Y., Datta A., Brown L.M., Pankanti S., Large-scale vehicle detection, indexing, and search in urban surveillance videos, IEEE Trans. Multimed. 14 (1) (2012) 28–42.

[35]

Geroliminis N., Skabardonis A., Real time vehicle reidentification and performance measures on signalized arterials, in: Intelligent Transportation Systems Conference, 2006. ITSC’06. IEEE, IEEE, 2006, pp. 188–193.

[36]

Gimeno R.V.C., Celda A.G., Pla-Castells M., Plume J.M., Improving similarity measures for re-identification of vehicles using amr sensors, in: Information, Communications and Signal Processing (ICICS) 2013 9th International Conference on, IEEE, 2013, pp. 1–5.

[37]

Golob T.F., Recker W.W., Alvarez V.M., Freeway safety as a function of traffic flow, Accid. Anal. Prev. 36 (6) (2004) 933–946.

[38]

Gong S., Cristani M., Loy C.C., Hospedales T.M., The re-identification challenge, in: Person Re-Identification, Springer, 2014, pp. 1–20.

[39]

Guilbert D., Le Bastard C., Ieng S.S., Wang Y., Re-identification by inductive loop detector: Experimentation on target origindestination matrix, in: Intelligent Vehicles Symposium (IV), 2013 IEEE, IEEE, 2013, pp. 1421–1427.

[40]

Guo Y., Rao C., Samarasekera S., Kim J., Kumar R., Sawhney H., Matching vehicles under large pose transformations using approximate 3d models and piecewise mrf model, in: Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, IEEE, 2008, pp. 1–8.

[41]

Guo Y.F., Wu L., Lu H., Feng Z., Xue X., Null foley–sammon transform, Pattern recognition 39 (11) (2006) 2248–2251.

[42]

Hage R.M., Le E., Peyret F., Meizel D., et al., Link travel time estimation in urban areas by detectors and probe vehicles fusion, in: 2nd International Conference on Models and Technologies for Intelligent Transportation Systems, 2011.

[43]

Hall D.L., McMullen S.A., Mathematical Techniques in Multisensor Data Fusion, Artech House, 2004.

[44]

Hellwich O., Wiedemann C., Multisensor data fusion for automated scene interpretation, in: Image and Signal Processing for Remote Sensing V, Vol. 3871, International Society for Optics and Photonics, 1999, pp. 284–296.

[45]

Hellwich O., Wiedemann C., Object extraction from high-resolution multisensor image data, in: Third International Conference Fusion of Earth Data, Sophia Antipolis, Vol. 115, 2000.

[46]

Hou T., Wang S., Qin H., Vehicle matching and recognition under large variations of pose and illumination, in: Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on, IEEE, 2009, pp. 24–29.

[47]

Hsu C.Y., Kang L.W., Liao H.Y.M., Cross-camera vehicle tracking via affine invariant object matching for video forensics applications, in: Multimedia and Expo (ICME), 2013 IEEE International Conference on, IEEE, 2013, pp. 1–6.

[48]

Hu C., Bai X., Qi L., Chen P., Xue G., Mei L., Vehicle color recognition with spatial pyramid deep learning, IEEE Trans. Intell. Transp. Syst. 16 (5) (2015) 2925–2934.

[49]

Javed O., Shafique K., Rasheed Z., Shah M., Modeling inter-camera space–time and appearance relationships for tracking across non-overlapping views, Comput. Vis. Image Underst. 109 (2) (2008) 146–162.

[50]

Jeng, S.T., 2007. Real-time vehicle reidentification system for freeway performance measurements.

[51]

JENG S.T.C., Chu L., Vehicle reidentification with the inductive loop signature technology, J. East. Asia Soc. Transp. Stud. 10 (2013) 1896–1915.

[52]

Kanacı A., Zhu X., Gong S., Vehicle re-identification by fine-grained cross-level deep learning, in: BMVC AMMDS Workshop, Vol. 2, 2017.

[53]

Kell, J.H., Fullerton, I.J., Mills, M.K., 1990. Traffic detector handbook. Technical Report.

[54]

Kerekes R.A., Karnowski T.P., Kuhn M., Moore M.R., Stinson B., Tokola R., Anderson A., Vann J.M., Vehicle classification and identification using multi-modal sensing and signal learning, in: Vehicular Technology Conference (VTC Spring), 2017 IEEE 85th, IEEE, 2017, pp. 1–5.

[55]

Klein L.A., Dempster-shafer data fusion at the traffic management center, in: Transportation Research Board 79th Annual Meeting, 2000.

[56]

Klein L.A., Kelley M.R., Mills M.K., Evaluation of overhead and in-ground vehicle detector technologies for traffic flow measurement, J. Test. Eval. 25 (2) (1997) 205–214.

[57]

Kong Q.J., Li Z., Chen Y., Liu Y., An approach to urban traffic state estimation by fusing multisource information, IEEE Trans. Intell. Transp. Syst. 10 (3) (2009) 499–511.

[58]

Krause J., Stark M., Deng J., Fei-Fei L., 3d object representations for fine-grained categorization, in: Computer Vision Workshops (ICCVW), 2013 IEEE International Conference on, IEEE, 2013, pp. 554–561.

[59]

Kreeger K., McConnell R., Structural range image target matching for automated link travel time computation, in: Intelligent Transportation: Realizing the Future. Abstracts of the Third World Congress on Intelligent Transport SystemsITS America, 1996.

[60]

Krizhevsky A., Sutskever I., Hinton G.E., Imagenet classification with deep convolutional neural networks, in: Advances in neural information processing systems, 2012, pp. 1097–1105.

Digital Library

[61]

Kuhne R.D., Immes S., Freeway control systems for using section-related traffic variable detection, in: Pacific Rim TransTech Conference (1993: Seattle, Wash.). Proceedings Pacific Rim TransTech Conference. Vol. 1, 1993.

[62]

Kwon J., Coifman B., Bickel P., Day-to-day travel-time trends and travel-time prediction from loop-detector data, Transpo. Res. Rec. J. Transp. Res. Board (1717) (2000) 120–129.

[63]

Kwong K., Kavaler R., Rajagopal R., Varaiya P., Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors, Transp. Res. C 17 (6) (2009) 586–606.

[64]

Lämmer S., Helbing D., Self-control of traffic lights and vehicle flows in urban road networks, J. Stat. Mech. Theory Exp. 2008 (04) (2008) P04019.

[65]

Leonard J., Sensor fusion for surgical applications, in: Dayton Section Symposium, 1998. The 15th Annual AESS/IEEE, IEEE, 1998, pp. 37–44.

[66]

Li, Y., Li, Y., Yan, H., Liu, J., 2017b. Deep joint discriminative learning for vehicle re-identification and retrieval.

[67]

Li X., Yuan M., Jiang Q., Li G., Vrid-1: A basic vehicle re-identification dataset for similar vehicles, in: Intelligent Transportation Systems (ITSC), 2017 IEEE 20th International Conference on, IEEE, 2017, pp. 1–8.

[68]

Liu H., Danczyk A., Brewer R., Starr R., Evaluation of cell phone traffic data in minnesota, Transp. Res. Rec. J. Transp. Res. Board (2086) (2008) 1–7.

[69]

Liu X., Liu W., Ma H., Fu H., Large-scale vehicle re-identification in urban surveillance videos, in: Multimedia and Expo (ICME), 2016 IEEE International Conference on, IEEE, 2016, pp. 1–6.

[70]

Liu X., Liu W., Mei T., Ma H., A deep learning-based approach to progressive vehicle re-identification for urban surveillance, in: European Conference on Computer Vision, Springer, 2016, pp. 869–884.

[71]

Liu X., Liu W., Mei T., Ma H., Provid: Progressive and multimodal vehicle reidentification for large-scale urban surveillance, IEEE Trans. Multimed. 20 (3) (2018) 645–658.

Digital Library

[72]

Liu H., Tian Y., Yang Y., Pang L., Huang T., Deep relative distance learning: Tell the difference between similar vehicles, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2167–2175.

[73]

Lunt, G., 0000. Vehicle re-identification using induction loop data.

[74]

Maaloul B., Taleb-Ahmed A., Niar S., Harb N., Valderrama C., Adaptive video-based algorithm for accident detection on highways, in: Industrial Embedded Systems (SIES), 2017 12th IEEE International Symposium on, IEEE, 2017, pp. 1–6.

[75]

Mallikarjuna C., Phanindra A., Rao K.R., Traffic data collection under mixed traffic conditions using video image processing, J. Transp. Eng. 135 (4) (2009) 174–182.

[76]

Manual H.C., Highway Capacity Manual, 2000, p. 11.

[77]

Mazloumi E., Currie G., Rose G., Using gps data to gain insight into public transport travel time variability, J. Transp. Eng. 136 (7) (2009) 623–631.

[78]

Müller M., Dynamic time warping, in: Information Retrieval for Music and Motion, Springer, 2007, pp. 69–84.

[79]

Ndoye, M., Totten, V., Carter, B., Bullock, D.M., Krogmeier, J.V., 2008. Vehicle detector signature processing and vehicle reidentification for travel time estimation. Technical Report.

[80]

Ndoye M., Totten V., Krogmeier J.V., Bullock D.M., A signal processing framework for vehicle re-identification and travel time estimation, in: Intelligent Transportation Systems, 2009. ITSC’09. 12th International IEEE Conference on, IEEE, 2009, pp. 1–6.

[81]

Ndoye M., Totten V.F., Krogmeier J.V., Bullock D.M., Sensing and signal processing for vehicle reidentification and travel time estimation, IEEE Trans. Intell. Transp. Syst. 12 (1) (2011) 119–131.

[82]

Oh C., Ritchie S.G., Jeng S.T., Anonymous vehicle reidentification using heterogeneous detection systems, IEEE Trans. Intell. Transp. Syst. 8 (3) (2007) 460–469.

[83]

Oh C., Tok A., Ritchie S.G., Real-time freeway level of service using inductive-signature-based vehicle reidentification system, IEEE Trans. Intell. Transp. Syst. 6 (2) (2005) 138–146.

[84]

Prinsloo J., Malekian R., Accurate vehicle location system using rfid, an internet of things approach, Sensors 16 (6) (2016) 825.

[85]

Ramachandran R.P., Arr G., Sun C., Ritchie S.G., A pattern recognition and feature fusion formulation for vehicle reidentification in intelligent transportation systems, in: Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on, Vol. 4, IEEE, 2002, pp. IV–3840.

[86]

Ramnath K., Sinha S.N., Szeliski R., Hsiao E., Car make and model recognition using 3d curve alignment, in: Applications of Computer Vision (WACV), 2014 IEEE Winter Conference on, IEEE, 2014, pp. 285–292.

[87]

Ren S., He K., Girshick R., Sun J., Faster r-cnn: towards real-time object detection with region proposal networks, IEEE Trans. Pattern Anal. Mach. Intell. 39 (6) (2017) 1137–1149.

Digital Library

[88]

Roberts C.M., Radio frequency identification (rfid), Comput. Secur. 25 (1) (2006) 18–26.

[89]

Rowley H.A., Baluja S., Kanade T., Neural network-based face detection, IEEE Trans. Pattern Anal. Mach. Intell. 20 (1) (1998) 23–38.

Digital Library

[90]

Saghafi M.A., Hussain A., Saad M.H.M., Tahir N.M., Zaman H.B., Hannan M., Appearance-based methods in re-identification: a brief review, in: Signal Processing and its Applications (CSPA), 2012 IEEE 8th International Colloquium on, IEEE, 2012, pp. 404–408.

[91]

Sanchez R.O., Flores C., Horowitz R., Rajagopal R., Varaiya P., Vehicle re-identification using wireless magnetic sensors: Algorithm revision, modifications and performance analysis, in: Vehicular Electronics and Safety (ICVES), 2011 IEEE International Conference on, IEEE, 2011, pp. 226–231.

[92]

Shan Y., Sawhney H.S., Kumar R., Vehicle identification between non-overlapping cameras without direct feature matching, in: Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, Vol. 1, IEEE, 2005, pp. 378–385.

[93]

Shan Y., Sawhney H.S., Kumar R., Unsupervised learning of discriminative edge measures for vehicle matching between nonoverlapping cameras, IEEE Trans. Pattern Anal. Mach. Intell. 30 (4) (2008) 700–711.

[94]

Sharma, R.K., 1999. Probabilistic model-based multisensor image fusion.

[95]

Shen Y., Xiao T., Li H., Yi S., Wang X., Learning deep neural networks for vehicle re-id with visual-spatio-temporal path proposals, in: Proceedings of the IEEE Conference on Computer Vision, 2017.

[96]

Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

[97]

Smith, B.L., Zhang, H., Fontaine, M., Green, M., 2003. Cellphone probes as an ATMS tool. Technical Report. The Center.

[98]

Sochor J., Herout A., Havel J., Boxcars: 3d boxes as cnn input for improved fine-grained vehicle recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 3006–3015.

[99]

Song H.O., Xiang Y., Jegelka S., Savarese S., Deep metric learning via lifted structured feature embedding, in: Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on, IEEE, 2016, pp. 4004–4012.

[100]

Sun C.C., Arr G.S., Ramachandran R.P., Ritchie S.G., Vehicle reidentification using multidetector fusion, IEEE Trans. Intell. Transp. Syst. 5 (3) (2004) 155–164.

[101]

Sun Z., Bebis G., Miller R., On-road vehicle detection: A review, IEEE Trans. Pattern Anal. Mach. Intell. 28 (5) (2006) 694–711.

[102]

Sun C., Ritchie S.G., Tsai K., Jayakrishnan R., Use of vehicle signature analysis and lexicographic optimization for vehicle reidentification on freeways, Transp. Res. C 7 (4) (1999) 167–185.

[103]

Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A., et al., Going Deeper with Convolutions, IEEE, 2015.

[104]

Takeda H., Farsiu S., Milanfar P., Kernel regression for image processing and reconstruction, IEEE Trans. Image Process. 16 (2) (2007) 349–366.

Digital Library

[105]

Tang Y., Wu D., Jin Z., Zou W., Li X., Multi-modal metric learning for vehicle re-identification in traffic surveillance environment, in: Image Processing (ICIP), 2017 IEEE International Conference on, IEEE, 2017, pp. 2254–2258.

[106]

Tian Y., Dong H.h., Jia L.m., Li S.y., A vehicle re-identification algorithm based on multi-sensor correlation, J. Zhejiang Univ. Sci. C 15 (5) (2014) 372–382.

[107]

Turner, S.M., Eisele, W.L., Benz, R.J., Holdener, D.J., 1998. Travel time data collection handbook. Technical Report.

[108]

Vishnu V.M., Rajalakshmi M., Nedunchezhian R., Intelligent traffic video surveillance and accident detection system with dynamic traffic signal control, Cluster Comput. (2017) 1–13.

[109]

Vuagnoux M., Pasini S., An improved technique to discover compromising electromagnetic emanations, in: Electromagnetic Compatibility (EMC), 2010 IEEE International Symposium on, IEEE, 2010, pp. 121–126.

[110]

Waltz E., Llinas J., et al., Multisensor Data Fusion, Vol. 685, Artech house Boston, 1990.

[111]

Wan E.A., Van Der Merwe R., The unscented kalman filter, in: Kalman Filtering and Neural Networks, Vol. 5, 2001, pp. 221–280.

[112]

Wang X., Intelligent multi-camera video surveillance: A review, Pattern Recognit. Lett. 34 (1) (2013) 3–19.

Digital Library

[113]

Wang J., Indra-Payoong N., Sumalee A., Panwai S., Vehicle reidentification with self-adaptive time windows for real-time travel time estimation, IEEE Trans. Intell. Transp. Syst. 15 (2) (2014) 540–552.

[114]

Wang Z., Tang L., Liu X., Yao Z., Yi S., Shao J., Yan J., Wang S., Li H., Wang X., Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 379–387.

[115]

Watchar N., Approximate license plate string matching for vehicle re-identification, in: Advanced Video and Signal Based Surveillance (AVSS), 2017 14th IEEE International Conference on, IEEE, 2017, pp. 1–6.

[116]

Woesler R., Fast extraction of traffic parameters and reidentification of vehicles from video data, in: Intelligent Transportation Systems, 2003. Proceedings. 2003 IEEE, Vol. 1, IEEE, 2003, pp. 774–778.

[117]

Wunnava, S.V., Yen, K., Babij, T., Zavaleta, R., Romero, R., Archilla, C., 2007. Travel time estimation using cell phones (ttecp) for highways and roadways.

[118]

Xiang Y., Choi W., Lin Y., Savarese S., Data-driven 3d voxel patterns for object category recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1903–1911.

[119]

Yan K., Tian Y., Wang Y., Zeng W., Huang T., Exploiting multi-grain ranking constraints for precisely searching visually-similar vehicles, in: The IEEE International Conference on Computer Vision (ICCV), 2017.

[120]

Yang G.Z., Andreu-Perez J., Hu X., Thiemjarus S., Multi-sensor fusion, in: Body sensor networks, Springer, 2014, pp. 301–354.

[121]

Yang L., Luo P., Change Loy C., Tang X., A large-scale car dataset for fine-grained categorization and verification, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3973–3981.

[122]

Ygnace, J.L., Drane, C., Yim, Y., De Lacvivier, R., 2000. Travel time estimation on the san francisco bay area network using cellular phones as probes.

[123]

Yim, Y., Cayford, R., 2001. Investigation of vehicles as probes using global positioning system and cellular phone tracking: field operational test.

[124]

Yokota T., Inoue T., Nagai T., Kobayahsi Y., Takagi K., Travel time measuring system based on platoon matching: a field study, in: Intelligent Transportation: Realizing the Benefits. Proceedings of the 1996 Annual Meeting of ITS America. ITS America, 1996.

[125]

Yong D., WenKang S., ZhenFu Z., Qi L., Combining belief functions based on distance of evidence, Decis. Support Syst. 38 (3) (2004) 489–493.

Digital Library

[126]

Zapletal D., Herout A., Vehicle re-identification for automatic video traffic surveillance, in: Computer Vision and Pattern Recognition Workshops (CVPRW), 2016 IEEE Conference on, IEEE, 2016, pp. 1568–1574.

[127]

Zha Z.J., Mei T., Wang Z., Hua X.S., Building a comprehensive ontology to refine video concept detection, in: Proceedings of the international workshop on Workshop on multimedia information retrieval, ACM, 2007, pp. 227–236.

[128]

Zhang B., Reliable classification of vehicle types based on cascade classifier ensembles, IEEE Trans. Intell. Transp. Syst. 14 (1) (2013) 322–332.

[129]

Zhang, C., Heidemann, J., 2011. Evaluating signature matching in a multisensor vehicle classification system (extended). Technical Report. Citeseer.

[130]

Zhang C., Liu W., Ma H., Fu H., Siamese neural network based gait recognition for human identification, in: Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on, IEEE, 2016, pp. 2832–2836.

[131]

Zhang Y., Liu D., Zha Z.J., Improving triplet-wise training of convolutional neural network for vehicle re-identification, in: Multimedia and Expo (ICME), 2017 IEEE International Conference on, IEEE, 2017, pp. 1386–1391.

[132]

Zhang J., Wang F.Y., Wang K., Lin W.H., Xu X., Chen C., Data-driven intelligent transportation systems: a survey, IEEE Trans. Intell. Transp. Syst. 12 (4) (2011) 1624–1639.

Digital Library

[133]

Zhang H.s., Zhang Y., Yao D.y., Hu D.c., An architecture of traffic state analysis based on multi-sensor fusion, in: Intelligent Vehicles Symposium, 2005. Proceedings. IEEE, IEEE, 2005, pp. 855–860.

[134]

Zheng Y., Capra L., Wolfson O., Yang H., Urban computing: concepts, methodologies, and applications, ACM Trans. Intell. Syst. Technol. (TIST) 5 (3) (2014) 38.

[135]

Zheng Q., Liang C., Fang W., Xiang D., Zhao X., Ren C., Chen J., Car re-identification from large scale images using semantic attributes, in: Multimedia Signal Processing (MMSP), 2015 IEEE 17th International Workshop on, IEEE, 2015, pp. 1–5.

[136]

Zhou Y., Liu L., Shao L., Vehicle re-identification by deep hidden multi-view inference, IEEE Trans. Image Process. (2018).

[137]

Zhou Y., Liu L., Shao L., Mellor M., Dave: a unified framework for fast vehicle detection and annotation, in: European Conference on Computer Vision, Springer, 2016, pp. 278–293.

[138]

Zhou X., Mahmassani H.S., Dynamic origin-destination demand estimation using automatic vehicle identification data, IEEE Trans. Intell. Transp. Syst. 7 (1) (2006) 105–114.

Cited By

Lv KChen HZhao CTu KChen JLi YLi BLin Y(2024)Style Variable and Irrelevant Learning for Generalizable Person Re-identificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/367100320:9(1-22)Online publication date: 6-Jun-2024
https://dl.acm.org/doi/10.1145/3671003
Wu DFang ZSun QChen LHu HWang FGao YBaeza-Yates RBonchi F(2024)TrajRecovery: An Efficient Vehicle Trajectory Recovery Framework based on Urban-Scale Traffic Camera RecordsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671558(5979-5990)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671558
Zhang HZhou WLiu GWang ZQian Z(2024)Fine-Grained Vehicle Make and Model Recognition Framework Based on Magnetic FingerprintIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.337488825:8(8460-8472)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3374888
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Index Terms

A survey of advances in vision-based vehicle re-identification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision tasks
        Scene understanding
  2. Machine learning

Index terms have been assigned to the content through auto-classification.

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cover image Computer Vision and Image Understanding

Computer Vision and Image Understanding Volume 182, Issue C

May 2019

93 pages

ISSN:1077-3142

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Elsevier Inc.

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Elsevier Science Inc.

United States

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Published: 01 May 2019

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Cited By

Lv KChen HZhao CTu KChen JLi YLi BLin Y(2024)Style Variable and Irrelevant Learning for Generalizable Person Re-identificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/367100320:9(1-22)Online publication date: 6-Jun-2024
https://dl.acm.org/doi/10.1145/3671003
Wu DFang ZSun QChen LHu HWang FGao YBaeza-Yates RBonchi F(2024)TrajRecovery: An Efficient Vehicle Trajectory Recovery Framework based on Urban-Scale Traffic Camera RecordsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671558(5979-5990)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671558
Zhang HZhou WLiu GWang ZQian Z(2024)Fine-Grained Vehicle Make and Model Recognition Framework Based on Magnetic FingerprintIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.337488825:8(8460-8472)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3374888
He CWang DCai ZZeng JFu F(2024)A Vehicle Matching Algorithm by Maximizing Travel Time Probability Based on Automatic License Plate Recognition DataIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.335862525:8(9103-9114)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3358625
Zhang CZheng SWu HGu ZSun WYang L(2024)AttentionTrack: Multiple Object Tracking in Traffic Scenarios Using Features AttentionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.331522225:2(1661-1674)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TITS.2023.3315222
Zheng WWang F(2024)Do the best of all togetherInformation Fusion10.1016/j.inffus.2024.102612113:COnline publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1016/j.inffus.2024.102612
Qian YBarthelemy JKaruppiah EPerez P(2024)Identifying Re-identification Challenges: Past, Current and Future TrendsSN Computer Science10.1007/s42979-024-03271-95:7Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1007/s42979-024-03271-9
Mei CYou XTeng SLYU X(2024)TL-RelD: Tight-Loose Pairwise Loss for Object Re-IdentificationPattern Recognition and Computer Vision10.1007/978-981-97-8858-3_12(172-185)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1007/978-981-97-8858-3_12
Liu YKong CLi YZhang PYu H(2023)Mask Multi-Head Attention with Partition Network for Vehicle Re-IdentificationProceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things10.1145/3603781.3603852(398-402)Online publication date: 26-May-2023
https://dl.acm.org/doi/10.1145/3603781.3603852
Biondi NPernici FBruni MDel Bimbo A(2023)CoReS: Compatible Representations via StationarityIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.325954245:8(9567-9582)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TPAMI.2023.3259542
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