Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Nguyen, Dat Tien; Hong, Hyung Gil; Kim, Ki Wan; Park, Kang Ryoung

doi:10.3390/s17030605

Cited by 489 publications

(181 citation statements)

References 55 publications

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“…Jungling et al (Jungling and Arens 2010) used infrared(IR) video for Re-ID, but they only considered the IR-IR video matching. Nguyen et al (Nguyen et al 2017) firstly applied person re-identification models to visiblethermal images. Wu et al (Wu, Zheng, and Lai 2017) designed a depth shape descriptor which is robust to rotation and noises.…”

Section: Related Workmentioning

confidence: 99%

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HSME: Hypersphere Manifold Embedding for Visible Thermal Person Re-Identification

Wang

et al. 2019

AAAI

238

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Person Re-identification(re-ID) has great potential to contribute to video surveillance that automatically searches and identifies people across different cameras. Heterogeneous person re-identification between thermal(infrared) and visible images is essentially a cross-modality problem and important for night-time surveillance application. Current methods usually train a model by combining classification and metric learning algorithms to obtain discriminative and robust feature representations. However, the combined loss function ignored the correlation between classification subspace and feature embedding subspace. In this paper, we use Sphere Softmax to learn a hypersphere manifold embedding and constrain the intra-modality variations and cross-modality variations on this hypersphere. We propose an end-to-end dualstream hypersphere manifold embedding network(HSMEnet) with both classification and identification constraint. Meanwhile, we design a two-stage training scheme to acquire decorrelated features, we refer the HSME with decorrelation as D-HSME. We conduct experiments on two crossmodality person re-identification datasets. Experimental results demonstrate that our method outperforms the state-of-the-art methods on two datasets. On RegDB dataset, rank-1 accuracy is improved from 33.47% to 50.85%, and mAP is improved from 31.83% to 47.00%.

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Section: Related Workmentioning

confidence: 99%

“…Two public cross-modality person reidentificaition datasets are adopted to evaluate our algorithm. (Nguyen et al 2017) provided a visible-thermal dataset RegDB, which contains 412 persons. Each person has 10 different visible and thermal images captured by visible and thermal camera.…”

Section: Datasets and Evaluationmentioning

confidence: 99%

HSME: Hypersphere Manifold Embedding for Visible Thermal Person Re-Identification

Wang

et al. 2019

AAAI

238

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“…For texture and shape-based body recognition, Varior et al [ 4 ] used the Siamese CNN (S-CNN) architecture. Nguyen et al [ 5 ] obtained image features using AlexNet-CNN and then evaluated the recognition using PCA and support vector machine (SVM). Shi et al [ 6 ] used the S-CNN architecture reported in an extant study [ 4 ].…”

Section: Related Workmentioning

confidence: 99%

Face and Body-Based Human Recognition by GAN-Based Blur Restoration

Koo

Cho

Baek

et al. 2020

Sensors

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The long-distance recognition methods in indoor environments are commonly divided into two categories, namely face recognition and face and body recognition. Cameras are typically installed on ceilings for face recognition. Hence, it is difficult to obtain a front image of an individual. Therefore, in many studies, the face and body information of an individual are combined. However, the distance between the camera and an individual is closer in indoor environments than that in outdoor environments. Therefore, face information is distorted due to motion blur. Several studies have examined deblurring of face images. However, there is a paucity of studies on deblurring of body images. To tackle the blur problem, a recognition method is proposed wherein the blur of body and face images is restored using a generative adversarial network (GAN), and the features of face and body obtained using a deep convolutional neural network (CNN) are used to fuse the matching score. The database developed by us, Dongguk face and body dataset version 2 (DFB-DB2) and ChokePoint dataset, which is an open dataset, were used in this study. The equal error rate (EER) of human recognition in DFB-DB2 and ChokePoint dataset was 7.694% and 5.069%, respectively. The proposed method exhibited better results than the state-of-art methods.

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“…Most of the existing datasets are for face recognition and include only frontal images [7]. To our knowledge, there are only two thermal datasets for human re-identification that do not focus on faces [19,22]. However, these datasets only contain thermal images of people walking along a corridor or in front of a building.…”

Section: Thermal Re-identification Datasetmentioning

confidence: 99%

“…Although thermal images are widely used in computer vision, especially for face recognition, there is no benchmark dataset for re-identification. To our knowledge, there are two datasets about people walking i) along a corridor [19] and ii) outside a building [22], both recorded by a fixed camera in a surveillance setup, not covering the case of domestic environments from a robot perspective. Thus, we have collected a thermal re-identification dataset, which is publicly available, with the camera mounted on a mobile service robot (see Fig.…”

Section: Introductionmentioning

confidence: 99%

Human Re-Identification with a Robot Thermal Camera Using Entropy-Based Sampling

Coşar

Bellotto

2019

J Intell Robot Syst

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Human re-identification is an important feature of domestic service robots, in particular for elderly monitoring and assistance, because it allows them to perform personalized tasks and human-robot interactions. However vision-based reidentification systems are subject to limitations due to human pose and poor lighting conditions. This paper presents a new re-identification method for service robots using thermal images. In robotic applications, as the number and size of thermal datasets is limited, it is hard to use approaches that require huge amount of training samples. We propose a re-identification system that can work using only a small amount of data. During training, we perform entropy-based sampling to obtain a thermal dictionary for each person. Then, a symbolic representation is produced by converting each video into sequences of dictionary elements. Finally, we train a classifier using this symbolic representation and geometric distribution within the new representation domain. The experiments are performed on a new thermal dataset for human re-identification, which includes various situations of human motion, poses and occlusion, and which is made publicly available for research purposes. The proposed approach has been tested on this dataset and its improvements over standard approaches have been demonstrated.

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Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Cited by 489 publications

References 55 publications

HSME: Hypersphere Manifold Embedding for Visible Thermal Person Re-Identification

HSME: Hypersphere Manifold Embedding for Visible Thermal Person Re-Identification

Face and Body-Based Human Recognition by GAN-Based Blur Restoration

Human Re-Identification with a Robot Thermal Camera Using Entropy-Based Sampling

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