Person Re-identification by Articulated Appearance Matching

Cheng, Dong; Cristani, Marco

doi:10.1007/978-1-4471-6296-4_7

Cited by 30 publications

(20 citation statements)

References 38 publications

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“…Another difference of our work is the matching procedure. While [8,7] do not discuss the pose estimation errors which prevalently exist in real-world datasets, we show that these errors make rigid feature learning/matching with only the PoseBox yield inferior results to the original image, and that the three-stream PoseBox fusion network effectively alleviates this problem.…”

Section: Introductionmentioning

confidence: 82%

Pose-Invariant Embedding for Deep Person Re-Identification

Zheng

Huang

et al. 2019

IEEE Trans. on Image Process.

461

257

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Pedestrian misalignment, which mainly arises from detector errors and pose variations, is a critical problem for a robust person re-identification (re-ID) system. With bad alignment, the background noise will significantly compromise the feature learning and and matching process. To address this problem, this paper introduces the pose invariant embedding (PIE) as a pedestrian descriptor. First, in order to align pedestrians to a standard pose, the PoseBox structure is introduced, which is generated through pose estimation followed by affine transformations. Second, to reduce the impact of pose estimation errors and information loss during PoseBox construction, we design a Pose-Box fusion (PBF) CNN architecture that takes the original image, the PoseBox, and the pose estimation confidence as input. The proposed PIE descriptor is thus defined as the fully connected layer of the PBF network for the retrieval task. Experiments are conducted on the Market-1501, CUHK03, and VIPeR datasets. We show that Pose-Box alone yields decent re-ID accuracy, and that when integrated in the PBF network, the learned PIE descriptor produces competitive performance compared with the stateof-the-art approaches.

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Section: Introductionmentioning

confidence: 82%

Pose-Invariant Embedding for Deep Person Re-Identification

Zheng

Huang

et al. 2019

IEEE Trans. on Image Process.

461

257

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show abstract

“…There are many possible cues useful for a fine visual characterization, and we considered two types of features: color histograms and maximally stable color regions (MSCRs) [40]. This is the same signature already used in [10,11,39,41].…”

Section: Feature Extraction and Matchingmentioning

confidence: 99%

“…Like [11,41], we employed a color histogram and the MSCR blobs as the image signature. To match two signatures, we use the combination of Bhattacharyya distance for histogram signature and the MSCR distance for MSCR features as in [10,11,39,41].…”

Section: Feature Extraction and Matchingmentioning

confidence: 99%

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Exploiting Multiple Detections for Person Re-Identification

2018

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Re-identification systems aim at recognizing the same individuals in multiple cameras, and one of the most relevant problems is that the appearance of same individual varies across cameras due to illumination and viewpoint changes. This paper proposes the use of cumulative weighted brightness transfer functions (CWBTFs) to model these appearance variations. Different from recently proposed methods which only consider pairs of images to learn a brightness transfer function, we exploit such a multiple-frame-based learning approach that leverages consecutive detections of each individual to transfer the appearance. We first present a CWBTF framework for the task of transforming appearance from one camera to another. We then present a re-identification framework where we segment the pedestrian images into meaningful parts and extract features from such parts, as well as from the whole body. Jointly, both of these frameworks contribute to model the appearance variations more robustly. We tested our approach on standard multi-camera surveillance datasets, showing consistent and significant improvements over existing methods on three different datasets without any other additional cost. Our approach is general and can be applied to any appearance-based method.

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“…For instance, in [7,6,8], the body of each target is divided into smaller parts and evaluated with multiple color histograms, one for each part. Even though this method is simple and effective, it fails in the case of strong illumination changes.…”

Section: Introductionmentioning

confidence: 99%

Ensemble of different approaches for a reliable person re-identification system

Nanni

Munaro

Ghidoni

et al. 2016

Applied Computing and Informatics

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An ensemble of approaches for reliable person re-identification is proposed in this paper. The proposed ensemble is built combining widely used person re-identification systems using different color spaces and some variants of state-of-the-art approaches that are proposed in this paper. Different descriptors are tested, and both texture and color features are extracted from the images; then the different descriptors are compared using different distance measures (e.g., the Euclidean distance, angle, and the Jeffrey distance). To improve performance, a method based on skeleton detection, extracted from the depth map, is also applied when the depth map is available. The proposed ensemble is validated on three widely used datasets (CAVIAR4REID, IAS, and VIPeR), keeping the same parameter set of each approach constant across all tests to avoid overfitting and to demonstrate that the proposed system can be considered a general-purpose person re-identification system. Our experimental results show that the proposed system offers significant improvements over baseline approaches.

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Person Re-identification by Articulated Appearance Matching

Cited by 30 publications

References 38 publications

Pose-Invariant Embedding for Deep Person Re-Identification

Pose-Invariant Embedding for Deep Person Re-Identification

Exploiting Multiple Detections for Person Re-Identification

Ensemble of different approaches for a reliable person re-identification system

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