A Multiple Component Matching Framework for Person Re-identification

Satta, Riccardo; Fumera, Giorgio; Roli, Fabio; Cristani, Marco; Murino, Vittorio

doi:10.1007/978-3-642-24088-1_15

Cited by 44 publications

(72 citation statements)

References 11 publications

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“…In order to improve the performance of these representations in the context of person re-identification, several papers have proposed to use discriminative classifiers on top of them: these classifiers can be based on Adaboost [16,17], Rank SVM [15], Partial Least Squares (PLS), multi-feature learning [26] or multiple instance learning [27,28].…”

Section: Related Workmentioning

confidence: 99%

Covariance descriptor based on bio-inspired features for person re-identification and face verification

Ma¹,

Su²,

Jurie³

2014

Image and Vision Computing

249

137

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Avoiding the use of complicated pre-processing steps such as accurate face and body part segmentation or image normalization, this paper proposes a novel face/person image representation which can properly handle background and illumination variations. Denoted as gBiCov, this representation relies on the combination of Biologically Inspired Features (BIF) and Covariance descriptors [1]. More precisely, gBiCov is obtained by computing and encoding the difference between BIF features at different scales. The distance between two persons can then be efficiently measured by computing the Euclidean distance of their signatures, avoiding some time consuming operations in Riemannian manifold required by the use of Covariance descriptors. In addition, the recently proposed KISSME framework [2] is adopted to learn a metric adapted to the representation. To show the effectiveness of gBiCov, experiments are conducted on three person re-identification tasks (VIPeR, i-LIDS and ETHZ) and one face verification task (LFW), on which competitive results are obtained. As an example, the matching rate at rank 1 on the VIPeR dataset is of 31.11%, improving the best previously published result by more than 10%.

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Section: Related Workmentioning

confidence: 99%

Covariance descriptor based on bio-inspired features for person re-identification and face verification

Ma¹,

Su²,

Jurie³

2014

Image and Vision Computing

249

137

View full text Add to dashboard Cite

show abstract

“…Therefore, to generate EoC-STUS (Algorithm 2), first the non-target trajectories are sorted based on their proximity to the target class using Hausdorff distance [Edgar, 2007]. It measures the distance between two sets of samples as the maximum of the minimum distances between pairs of elements from the two sets [Satta et al, 2011]. The Hausdroff distance between all non-target trajectories and target trajectory is calculated as: …”

Section: Sorted Trajectory Under-sampling (Stus)mentioning

confidence: 99%

Classifier Ensembles with Trajectory Under-Sampling for Face Re-Identification

Soleymani

Granger

Fumera

2016

Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods

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Abstract:In person re-identification applications, an individual of interest may be covertly tracked and recognized based on trajectories of faces or other distinguishing information captured with video surveillance camera. However, a varying level of imbalance often exists between target and non-target facial captures, and this imbalance level may differ from what was considered during design. The performance of face classification systems typically declines in such cases because, to avoid bias towards the majority class (non-target), they tend to optimize the overall accuracy under a balance class assumption. Specialized classifier ensembles trained on balanced data, where non-target samples are selected through random under-sampling or cluster-based sampling, have been proposed in literature, but they suffer from loss of information and low diversity and accuracy. In this paper, a new ensemble method is proposed for generating a diverse pool of classifiers, each one trained on different levels of class imbalance and complexity for a greater diversity of opinion. Ensembles with Trajectory Under Sampling (EoC-TUS) allows to select subsets of non-target training data based on trajectories information. Variants of these ensembles can give more importance to the most efficient classifiers in identifying target samples, or define efficient and diverse decision boundaries by starting selection of trajectories from the farthest ones to the target class. For validation, experiments are conducted using videos captured in the Faces In Action dataset, and compared to several baseline techniques. The proposed EoC-TUS outperforms state-of-the-art techniques in terms of accuracy and diversity over a range of imbalance levels in the input video.

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“…Similarly, in [47], a high-dimensional signature composed by texture, gradient and color information is projected into a low-dimensional discriminant latent space by Partial Least Squares (PLS) reduction. Multiple Component Learning is casted into the re-id scenario, dubbing it a Multiple Component Matching and exploiting SDALF as a descriptor, in [46]. The descriptor proposed in [54] uses contextual visual knowledge coming from the surrounding people that form a group, assuming that groups can be detected.…”

Section: Related Workmentioning

confidence: 99%

SDALF: Modeling Human Appearance with Symmetry-Driven Accumulation of Local Features

Bazzani

Cristani

Murino

2014

Person Re-Identification

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In video surveillance, person re-identification (re-id) is probably the open challenge, when dealing with a camera network with non-overlapped fields of view. Re-id allows the association of different instances of the same person across different locations and time. A large number of approaches have emerged in the last five years, often proposing novel visual features specifically designed to highlight the most discriminant aspects of people, which are invariant to pose, scale and illumination. In this chapter, we follow this line, presenting a strategy with three important key-characteristics that differentiate it with respect to the state of the art: 1) a symmetry-driven method to automatically segment salient body parts, 2) an accumulation of features making the descriptor more robust to appearance variations, and 3) a person re-identification procedure cast as an image retrieval problem, which can be easily embedded into a multi-person tracking scenario, as the observation model.

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A Multiple Component Matching Framework for Person Re-identification

Cited by 44 publications

References 11 publications

Covariance descriptor based on bio-inspired features for person re-identification and face verification

Covariance descriptor based on bio-inspired features for person re-identification and face verification

Classifier Ensembles with Trajectory Under-Sampling for Face Re-Identification

SDALF: Modeling Human Appearance with Symmetry-Driven Accumulation of Local Features

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