Appearance-Based 3D Upper-Body Pose Estimation and Person Re-identification on Mobile Robots

Weinrich, Christoph; Volkhardt, Michael; Groß, Horst–Michael

doi:10.1109/smc.2013.748

Cited by 10 publications

(9 citation statements)

References 25 publications

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“…They achieve pose invariance by combining these features through a weighting scheme based on human body (vertical) axis of symmetry. Weinrich et al [14] detect the upper body pose for tracking the human subject in egocentric image frames. The local texture features of the upper body are learned and a generative 3D shape model is used for re-identification purpose.…”

Section: A Pose Based Methods In Re-idmentioning

confidence: 99%

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Pose Invariant Person Re-Identification using Robust Pose-transformation GAN

Karmakar¹,

Mishra²

2021

Preprint

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The objective of person re-identification (re-ID) is to retrieve a person's images from an image gallery, given a single instance of the person of interest. Despite several advancements, learning discriminative identity-sensitive and viewpoint invariant features for robust Person Re-identification is a major challenge owing to large pose variation of humans. This paper proposes a re-ID pipeline which utilizes the image generation capability of Generative Adversarial Networks combined with pose clustering and feature fusion to achieve pose invariant feature learning. The objective is to model a given person under different viewpoints and large pose changes and extract the most discriminative features from all the appearances. The pose transformational GAN (pt-GAN) module is trained to generate a person's image in any given pose. In order to identify the most significant poses for discriminative feature extraction, a Pose Clustering module is proposed. The given instance of the person is modelled in varying poses and these features are effectively combined through the Feature Fusion Network. The final re-ID model consisting of these 3 sub blocks, alleviates the pose dependence in person re-ID. Also, The proposed model is robust to occlusion, scale, rotation and illumination, providing a framework for viewpoint invariant feature learning. The proposed method outperforms the stateof-the-art GAN based models in 4 benchmark datasets. It also surpasses the state-of-the-art models that report higher re-ID accuracy in terms of improvement over baseline.The code snippets 1 of this work canbe found in https://github.com/arnabk001/Pose-Invariant-Person-Re-Identification-using-Robust-Pose-Transfromation-GAN

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Section: A Pose Based Methods In Re-idmentioning

confidence: 99%

“…Initial approaches of re-ID started with handcrafted features [9] [13] [14], where a combination of manually extracted features are used as the image descriptor. Although these methods provide good accuracy in smaller datasets, there have not been any significant improvement of these methods over the years.…”

Section: Related Workmentioning

confidence: 99%

Pose Invariant Person Re-Identification using Robust Pose-transformation GAN

Karmakar¹,

Mishra²

2021

Preprint

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“…The person is recognized using height, gait, and appearance features. The tracking information is also used in [29], where the identification is based on an appearance model, using particle swarm optimization to combine a precise upper body's pose estimation and appearance. In these approaches, re-identification is mainly used to recover human IDs during people tracking.…”

Section: Related Workmentioning

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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“…To capture appearance, geometry, and shape information of human body parts, features commonly extracted are silhouettes [31,32,33,34], contours [35,36], edges [37,38], etc. Silhouettes extract outlines of objects and are invariant to texture and lighting [32,128,224,225,226].…”

Section: Featuresmentioning

confidence: 99%

Human Pose Estimation from Monocular Images: A Comprehensive Survey

Gong

Zhang

Gonzàlez

et al. 2016

Sensors

130

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Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used.

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Appearance-Based 3D Upper-Body Pose Estimation and Person Re-identification on Mobile Robots

Cited by 10 publications

References 25 publications

Pose Invariant Person Re-Identification using Robust Pose-transformation GAN

Pose Invariant Person Re-Identification using Robust Pose-transformation GAN

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

Human Pose Estimation from Monocular Images: A Comprehensive Survey

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