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Erschienen in:
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2014 | OriginalPaper | Buchkapitel

1. The Re-identification Challenge

verfasst von : Shaogang Gong, Marco Cristani, Chen Change Loy, Timothy M. Hospedales

Erschienen in: Person Re-Identification

Verlag: Springer London

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Abstract

For making sense of the vast quantity of visual data generated by the rapid expansion of large-scale distributed multi-camera systems, automated person re-identification is essential. However, it poses a significant challenge to computer vision systems. Fundamentally, person re-identification requires to solve two difficult problems of ‘finding needles in haystacks’ and ‘connecting the dots’ by identifying instances and associating the whereabouts of targeted people travelling across large distributed space–time locations in often crowded environments. This capability would enable the discovery of, and reasoning about, individual-specific long-term structured activities and behaviours. Whilst solving the person re-identification problem is inherently challenging, it also promises enormous potential for a wide range of practical applications, ranging from security and surveillance to retail and health care. As a result, the field has drawn growing and wide interest from academic researchers and industrial developers. This chapter introduces the re-identification problem, highlights the difficulties in building person re-identification systems, and presents an overview of recent progress and the state-of-the-art approaches to solving some of the fundamental challenges in person re-identification, benefiting from research in computer vision, pattern recognition and machine learning, and drawing insights from video analytics system design considerations for engineering practical solutions. It also provides an introduction of the contributing chapters of this book. The chapter ends by posing some open questions for the re-identification challenge arising from emerging and future applications.

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Nächstes Kapitel Discriminative Image Descriptors for Person Re-identification
Literatur
1.
Keval, H.: CCTV control room collaboration and communication: does it work? In: Human Centred Technology Workshop (2006)
2.
Williams, D.: Effective CCTV and the challenge of constructing legitimate suspicion using remote visual images. J. Invest. Psychol. Offender Profiling 4(2), 97–107 (2007)CrossRef
3.
Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (2007)
4.
Prosser, B., Zheng, W., Gong, S., Xiang, T.: Person re-identification by support vector ranking. In: British Machine Vision Conference, pp. 21.1–21.11 (2010)
5.
Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: IEEE Conference Computer Vision and Pattern Recognition, pp. 2360–2367 (2010)
6.
Zheng, W., Gong, S., Xiang, T.: Re-identification by relative distance comparison. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 653–668 (2013)CrossRef
7.
Layne, R., Hospedales, T.M., Gong, S.: Person re-identification by attributes. In: British Machine Vision Conference (2012)
8.
Zhao, R., Ouyang, W., Wang, X.: Unsupervised salience learning for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition (2013)
9.
Bazzani, L., Cristani, M., Murino, V.: Symmetry-driven accumulation of local features for human characterization and re-identification. Comput. Vis. Image Underst. 117(2), 130–144 (2013)
10.
Madden, C., Cheng, E.D., Piccardi, M.: Tracking people across disjoint camera views by an illumination-tolerant appearance representation. Mach. Vis. Appl. 18(3), 233–247 (2007)CrossRefMATH
11.
Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: European Conference on Computer Vision, pp. 262–275 (2008)
12.
Bazzani, L., Cristani, M., Perina, A., Murino, V.: Multiple-shot person re-identification by chromatic and epitomic analyses. Pattern Recogn. Lett. 33(7), 898–903 (2012)CrossRef
13.
Bak, S., Corvee, E., Brémond, F., Thonnat, M.: Person re-identification using spatial covariance regions of human body parts. In: IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 435–440 (2010)
14.
Ma, B., Su, Y., Jurie, F.: Local descriptors encoded by fisher vectors for person re-identification. In: European Conference on Computer Vision, First International Workshop on Re-Identification, pp. 413–422 (2012)
15.
Zheng, W., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: IEEE Conference Computer Vision and Pattern Recognition, pp. 649–656 (2011)
16.
Avraham, T., Gurvich, I., Lindenbaum, M., Markovitch, S.: Learning implicit transfer for person re-identification. In: European Conference on Computer Vision, First International Workshop on Re-Identification, pp. 381–390 (2012)
17.
Hirzer, M., Beleznai, C., Roth, P., Bischof, H.: Person re-identification by descriptive and discriminative classification. In: Heyden, A., Kahl, F. (eds.) Image Analysis, pp. 91–102. Springer, New York (2011)
18.
Liu, C., Gong, S., Loy, C.C., Lin, X.: Person re-identification: what features are important? In: European Conference on Computer Vision, First International Workshop on Re-identification, pp. 391–401 (2012)
19.
Zheng, W., Gong, S., Xiang, T.: Transfer re-identification: from person to set-based verification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2650–2657 (2012)
20.
Li, W., Zhao, R., Wang, X.: Human reidentification with transferred metric learning. In: Asian Conference on Computer Vision, pp. 31–44 (2012)
21.
Zheng, W., Gong, S., Xiang, T.: Associating groups of people. In: British Machine Vision Conference, pp. 23.1–23.11 (2009)
22.
Loy, C.C., Xiang, T., Gong, S.: Time-delayed correlation analysis for multi-camera activity understanding. Int. J. Comput. Vision 90(1), 106–129 (2010)CrossRef
23.
Loy, C.C., Xiang, T., Gong, S.: Incremental activity modelling in multiple disjoint cameras. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1799–1813 (2012)CrossRef
24.
25.
Wang, X.G., Doretto, G., Sebastian, T., Rittscher, J., Tu, P.: Shape and appearance context modeling. In: International Conference on Computer Vision, pp. 1–8 (2007)
26.
Alahi, A., Vandergheynst, P., Bierlaire, M., Kunt, M.: Cascade of descriptors to detect and track objects across any network of cameras. Comput. Vis. Image Underst. 114(6), 624–640 (2010)
27.
Schwartz, W.R., Davis, L.S.: Learning discriminative appearance-based models using partial least squares. In: Brazilian Symposium on Computer Graphics and Image Processing, pp. 322–329 (2009)
28.
Cheng, D.S., Cristani, M., Stoppa, M., Bazzani, L., Murino, V.: Custom pictorial structures for re-identification. In: British Machine Vision Conference, pp. 68.1–68.11 (2011)
29.
Loy, C.C., Liu, C., Gong, S.: Person re-identification by manifold ranking. In: IEEE International Conference on Image Processing (2013)
30.
Gheissari, N., Sebastian, T., Hartley, R.: Person reidentification using spatiotemporal appearance. In: IEEE Conference Computer Vision and Pattern Recognition, pp. 1528–1535 (2006)
31.
Mignon, A., Jurie, F.: PCCA: A new approach for distance learning from sparse pairwise constraints. In: IEEE Conference Computer Vision and Pattern Recognition, pp. 2666–2672 (2012)
32.
Nortcliffe, T.: People Analysis CCTV Investigator Handbook. Home Office Centre of Applied Science and Technology, Holland (2011)
33.
Lampert, C.H., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 951–958 (2009)
34.
Siddiquie, B., Feris, R.S., Davis, L.S.: Image ranking and retrieval based on multi-attribute queries. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 801–808 (2011)
35.
Liu, J., Kuipers, B.: Recognizing human actions by attributes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3337–3344 (2011)
36.
Kumar, N., Berg, A., Belhumeur, P.: Describable visual attributes for face verification and image search. In: IEEE Trans. Pattern Anal. Mach. Intell. 33(10), 1962–1977 (2011)
37.
Porikli, F.: Inter-camera color calibration by correlation model function. In: IEEE International Conference on Image Processing (2003)
38.
Chen, K.W., Lai, C.C., Hung, Y.P., Chen, C.S.: An adaptive learning method for target tracking across multiple cameras. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
39.
D’Orazio, T., Mazzeo, P.L., Spagnolo, P.: Color brightness transfer function evaluation for non overlapping multi camera tracking. In: ACM/IEEE International Conference on Distributed Smart Cameras, pp. 1–6 (2009)
40.
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), 146–162 (2008)CrossRef
41.
Jeong, K., Jaynes, C.: Object matching in disjoint cameras using a color transfer approach. Mach. Vis. Appl. 19(5–6), 443–455 (2008)CrossRefMATH
42.
Lian, G., Lai, J.H., Suen, C.Y., Chen, P.: Matching of tracked pedestrians across disjoint camera views using CI-DLBP. IEEE Trans. Circuits Syst. Video Technol. 22(7), 1087–1099 (2012)CrossRef
43.
Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10, 207–244 (2009)MATH
44.
Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: International Conference on Machine learning, pp. 209–216 (2007)
45.
Guillaumin, M., Verbeek, J., Schmid, C.: Is that you? metric learning approaches for face identification. In: International Conference on Computer Vision, pp. 498–505 (2009)
46.
Kostinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2288–2295 (2012)
47.
Hirzer, M., Roth, P., Köstinger, M., Bischof, H.: Relaxed pairwise learned metric for person re-identification. In: European Conference on Computer Vision, pp. 780–793 (2012)
48.
Satta, R., Fumera, G., Roli, F., Cristani, M., Murino, V.: A multiple component matching framework for person re-identification. In: International Conference on Image analysis and Processing, pp. 140–149 (2011)
49.
Figueira, D., Bazzani, L., Quang, M.H., Cristani, M., Bernardino, A., Murino, V.: Semi-supervised multi-feature learning for person re-identification. In: IEEE International Conference on Advanced Video and Signal-Based Surveillance (2013)
50.
Wu, Y., Li, W., Minoh, M., Mukunoki, M.: Can feature-based inductive transfer learning help person re-identification? In: IEEE International Conference on Image Processing (2013)
51.
Makris, D., Ellis, T., Black, J.: Bridging the gaps between cameras. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 205–210 (2004)
52.
Tieu, K., Dalley, G., Grimson, W.E.L.: Inference of non-overlapping camera network topology by measuring statistical dependence. In: International Conference on Computer Vision, vol. 2, pp. 1842–1849 (2005)
53.
van den Hengel, A., Dick, A., Hill, R.: Activity topology estimation for large networks of cameras. In: IEEE International Conference on Video and Signal Based Surveillance (2006)
54.
Gong, S., Loy, C.C., Xiang, T.: Security and surveillance. In: Visual Analysis of Humans, pp. 455–472. Springer, New York (2011)
55.
Liu, C., Loy, C.C., Gong, S., Wang, G.: POP: Person re-identification post-rank optimisation. In: International Conference on Computer Vision (2013)
56.
Han, J., Bhanu, B.: Fusion of color and infrared video for moving human detection. Pattern Recogn. 40(6), 1771–1784 (2007)CrossRefMATH
57.
Correa, M., Hermosilla, G., Verschae, R., Ruiz-del Solar, J.: Human detection and identification by robots using thermal and visual information in domestic environments. J. Intell. Rob. Syst. 66(1–2), 223–243 (2012)CrossRef
58.
Hofmann, M., Geiger, J., Bachmann, S., Schuller, B., Rigoll, G.: The TUM gait from audio, image and depth (GAID) database: multimodal recognition of subjects and traits. J. Vis. Commun. Image Represent. (2013)
59.
Choudhury, T., Clarkson, B., Jebara, T., Pentland, A.: Multimodal person recognition using unconstrained audio and video. In: International Conference on Audio- and Video-Based Person Authentication, pp. 176–181 (1999)
60.
Choi, H., Park, U., Jain, A.: PTZ camera assisted face acquisition, tracking and recognition. In: IEEE International Conference on Biometrics: Theory, Applications and Systems (2010)
61.
Salvagnini, P., Bazzani, L., Cristani, M., Murino, V.: Person re-identification with a PTZ camera: an introductory study. In: IEEE International Conference on Image Processing (2013)
62.
Vezzani, R., Baltieri, D., Cucchiara, R.: People re-identification in surveillance and forensics: a survey. ACM Comput. Surv. 46(2), 1–36 (2014)
63.
Wang, X.: Intelligent multi-camera video surveillance: a review. Pattern Recogn. Lett. 34(1), 3–19 (2012)CrossRef
Metadaten
Titel
The Re-identification Challenge
verfasst von
Shaogang Gong
Marco Cristani
Chen Change Loy
Timothy M. Hospedales
Copyright-Jahr
2014
Verlag
Springer London
Buch
Person Re-Identification

Print ISBN: 978-1-4471-6295-7

Electronic ISBN: 978-1-4471-6296-4

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-1-4471-6296-4_1

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