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Person re-identification by symmetry-driven accumulation of local features

ABSTRACT

In this paper, we present an appearance-based method for person re-identification. It consists in the extraction of features that model three complementary aspects of the human appearance: the overall chromatic content, the spatial arrangement of colors into stable regions, and the presence of recurrent local motifs with high entropy. All this information is derived from different body parts, and weighted opportunely by exploiting symmetry and asymmetry perceptual principles. In this way, robustness against very low resolution, occlusions and pose, viewpoint and illumination changes is achieved. The approach applies to situations where the number of candidates varies continuously, considering single images or bunch of frames for each individual. It has been tested on several public benchmark datasets (ViPER, iLIDS, ETHZ), gaining new state-of-the-art performances.

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Available from: Loris Bazzani
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    • "The objective of this problem is to identify the matching image(s) from a set of gallery images for a given probe image, thereby saving labor intensive work of searching through the entire set of images for identifying the correct match. Main approaches that address this problem concentrate on developing a feature representation [1], [2], [3] and [4] for the images or learning a distance metric [5], [6], [7] [8] and [9] so that images belonging to the same person are closer to each other in a feature space. Despite the efforts of several researchers over the years, person re-identification still remains a challenging problem. "
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    ABSTRACT: This paper addresses the problem of matching pedestrians across multiple camera views, known as person re-identification. Variations in lighting conditions, environment and pose changes across camera views make re-identification a challenging problem. Previous methods address these challenges by designing specific features or by learning a distance function. We propose a hierarchical feature learning framework that learns invariant representations from labeled image pairs. A mapping is learned such that the extracted features are invariant for images belonging to same individual across views. To learn robust representations and to achieve better generalization to unseen data, the system has to be trained with a large amount of data. Critically, most of the person re-identification datasets are small. Manually augmenting the dataset by partial corruption of input data introduces additional computational burden as it requires several training epochs to converge. We propose a hierarchical network which incorporates a marginalization technique that can reap the benefits of training on large datasets without explicit augmentation. We compare our approach with several baseline algorithms as well as popular linear and non-linear metric learning algorithms and demonstrate improved performance on challenging publicly available datasets, VIPeR, CUHK01, CAVIAR4REID and iLIDS. Our approach also achieves the stateof-the-art results on these datasets.
    Preview · Article · Nov 2015
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    • "Specifically, in person Re-ID benchmarks the head is always located at the top of images, torso in the middle, and legs at the bottom. This typical structure has been exploited in designing discriminative features [10] "
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    • "Appearance of a person is the visible foreground image after background subtraction [2]. Similarly, appearance-based methods rely on clothes, visual parts or perceptual principles to extract features for object recognition [2] [3]. Texture on the other hand, contains structural arrangement of a surface and its relation with the environments [4]. "
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