Person Re-Identification Using Partial Least Squares Appearance Modelling

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10485)


Person Re-Identification is an important task in surveillance and security systems. Whilst most methods work by extracting features from the entire image, the best methods improve performance by prioritising features from foreground regions during the feature extraction stage. In this paper, we propose the use of a Partial Least Squares Regression model to predict the skeleton of a person, allowing us to prioritise features from a person’s limbs rather than from the background. Once the foreground area has been identified, we use the LOMO [9] and Salient Colour Names [21] features. We then use the XQDA [9] Distance Metric Learning method to compute the distance between each of the feature vectors. Experiments on VIPeR [4], QMUL GRID [12, 13, 14] and CUHK03 [8] data sets demonstrate significant improvements against state-of-the-art.


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of WarwickCoventryUK

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