Download Person Re-Identification by Shaogang Gong, Marco Cristani, Shuicheng Yan, Chen Change PDF

By Shaogang Gong, Marco Cristani, Shuicheng Yan, Chen Change Loy

The first e-book of its sort devoted to the problem of individual re-identification, this article presents an in-depth, multidisciplinary dialogue of contemporary advancements and cutting-edge tools. positive aspects: introduces examples of strong characteristic representations, studies salient characteristic weighting and choice mechanisms and examines the advantages of semantic attributes; describes the best way to segregate significant physique elements from historical past litter; examines using 3D intensity pictures and contextual constraints derived from the visible visual appeal of a bunch; stories ways to add move functionality and distance metric studying and discusses power options to problems with information scalability and id inference; investigates the constraints of present benchmark datasets, provides ideas for digital camera topology inference and describes options for making improvements to post-rank seek potency; explores the layout cause and implementation issues of creating a pragmatic re-identification system.

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It can be seen that on SEQ. 1 and 3, the proposed eBiCoV gives much better results than SDALF. It is even more obvious on SEQ. 3 for which our method’s CMC is equal to 100 % for N = 5, 10, which experimentally validates our descriptor. 4 Fisher Vector Encoded Local Descriptors for Person Re-identification This section presents our second descriptor and experimentally demonstrates its effectiveness on the two previously mentioned benchmarks. As explained in the Introduction, this descriptor is based on local features embedding.

Evaluation of the Image Descriptor In this section, our motivation is to evaluate the intrinsic properties of the descriptor. For this reason we do not use any metric learning but simply measure the similarity between two persons using the Euclidean distance between their representations. Evaluation of the simple feature vector. The core of our descriptor is the sevendimensional simple feature vector given by Eq. 11. This first set of experiments aims at validating this feature vector by comparing it with several alternatives, the rest of the framework being exactly the same.

Comput. Vis. Image Underst. 114(6), 624–640 (2010) 27. : Learning discriminative appearance-based models using partial least squares. In: Brazilian Symposium on Computer Graphics and Image Processing, pp. 322–329 (2009) 28. : Custom pictorial structures for re-identification. In: British Machine Vision Conference, pp. 11 (2011) 29. : Person re-identification by manifold ranking. In: IEEE International Conference on Image Processing (2013) 30. : Person reidentification using spatiotemporal appearance.

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