Unsupervised domain adaption for image-to-video person re-identification

Zhang, Xinyu; Li, Sen; Jing, Xiao‐Yuan; Ma, Fei; Zhu, Chen

doi:10.1007/s11042-019-08550-9

Cited by 6 publications

(4 citation statements)

References 38 publications

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“…Since videos always contain more valuable information, however, most of the existing IVPR models are under the supervision framework. In fact, marking enough training samples requires a lot of manpower, which limits their practical value [8]. The above arti-cles are very detailed about the related image recognition technology and the description of breakdancing.…”

Section: Related Workmentioning

confidence: 99%

Breakdancing Movement Based on Image Recognition Promotes Preschool Children’s Executive Function and Intervention Plan

Tian

Zhang

et al. 2022

Computational and Mathematical Methods in Medicine

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With the continuous development of science and technology, people can apply more and more technology to the cultivation of children’s abilities. In the process of cultivating children’s ability, the most fancy is the study of executive function, and this is the research topic of this article. In the past, training methods such as music, mindfulness, and exercise have been used in the study of children’s executive abilities to promote the development of preschool children’s executive functions. While various approaches have had some effect, researchers have been exploring more comprehensive approaches to effective training. This article is aimed at studying how to use image recognition technology to conduct an intervention analysis of breakdancing in promoting the executive function of preschool children. For this reason, this paper proposes image recognition technology based on deep learning neural network and conducts research, analysis, and improvement on related technologies obtained from deep learning. This makes it more suitable for the research topic of this article and design-related experiments and analysis to explore its related performance. The experimental results in this paper show that the improved image recognition technology has improved accuracy by 31.2%. And the performance of its algorithm is also improved by 21%, which can be very effective in monitoring preschool children during breakdancing.

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Section: Related Workmentioning

confidence: 99%

Breakdancing Movement Based on Image Recognition Promotes Preschool Children’s Executive Function and Intervention Plan

Tian

Zhang

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Recently, a series of methods have been presented to investigate the problem of image to video person re-id, and achieved interesting results [12]- [20]. Most of these methods focus on training a discriminative matching model by using a large number of labeled pedestrian image-video pairs.…”

Section: A Motivationmentioning

confidence: 99%

“…To relieve the requirement for the quantity of labeled image-video pairs, Zhang et al [20] presented a cross-modal feature generating and target information preserving transfer network, which transforms the features of unlabeled target sample into the source domain feature space while preserving target identity information, and uses a cross-modal loss term to eliminate the gap between pedestrian images and videos. By leveraging the labeled source dataset, this work reduced the requirement for labeled target data to some extent.…”

Section: A Image To Video Person Re-identificationmentioning

confidence: 99%

Heterogeneous Distance Learning Based on Kernel Analysis-Synthesis Dictionary for Semi-Supervised Image to Video Person Re-Identification

Zhu¹,

Jing

et al. 2020

IEEE Access

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Image to video person re-identification (IVPR), i.e., matching between pedestrian video and image, is an important task in practice. Although several methods have been presented for IVPR, most of these methods investigate the IVPR problem under the supervised setting, and require a large number of labeled image-video pairs for training. In this paper, we study the IVPR problem under the semi-supervised setting, and propose a Kernel Analysis-synthesis Dictionary based heterogeneous Distance Learning (KADDL) approach. Specifically, KADDL first learns two pairs of kernel analysis-synthesis dictionaries from the labeled and unlabeled training image-video data in the kernel space. With the learned dictionary pairs, the heterogeneous image and video features can be transformed into coding coefficients of the same representation space, such that the gap between image and video can be bridged. Then, KADDL learns a discriminative distance metric over the transformed coding coefficients, to make the coding coefficients of positive image-video pair become similar, while those of negative image-video pair dissimilar. To make better use of the unlabeled data, we further designed a reliability-based semi-supervised strategy for KADDL. Experiments on several publicly available pedestrian sequence datasets demonstrate the effectiveness of the proposed approach. INDEX TERMS Semi-supervised Image to video Person Re-identification, Distance learning, Kernel dictionary learning, coupled dictionary learning VOLUME 4, 2016 This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2020.3024289, IEEE Access Xiaoke Zhu et al.: KADDL for Semi-supervised Image to Video Person Re-id Probe image Gallery image Image-based person re-id Probe image Image to video person re-id Gallery video (c) Match Image feature Cross-domain match Image feature Video feature Image feature Probe video Gallery video Match Video feature Video feature Video-based person re-id

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“…The query image of image retrieval [3] is usually a simple single object, while the image in the database usually has many distractions. If we can extract the parts of the database image we need to carry out targeted retrieval, it will certainly improve the accuracy of image retrieval.…”

Section: Introductionmentioning

confidence: 99%

Bow image retrieval method based on SSD target detection

et al. 2020

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The query image is usually a simple and single object in image retrieval, and the reference images in the database usually have many distractions. The precision of image retrieval can be greatly improved If the target regions in the database image are extracted during retrieval. So this paper proposes a Bow image retrieval method based on SSD target detection. First, the training gallery is manually annotated to record the location and size information. Second, the SSD target detection model is trained with the labeled training gallery to obtain the target object SSD model. Third, the SSD model is used to locate the similar target regions of the reference image and the query graph. Finally, the target region information is mapped into the convolutional features, and these feature vectors are used for image similarity matching. The performance of the proposed method is evaluated on Paris6k, Oxford5k, Paris106k and Oxford105k databases. The experimental results show that the accuracy of image retrieval will be greatly improved by adding optimization methods in the proposed image retrieval framework. The image retrieval accuracy of this method is higher than that of similar methods in recent years.

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Unsupervised domain adaption for image-to-video person re-identification

Cited by 6 publications

References 38 publications

Breakdancing Movement Based on Image Recognition Promotes Preschool Children’s Executive Function and Intervention Plan

Breakdancing Movement Based on Image Recognition Promotes Preschool Children’s Executive Function and Intervention Plan

Heterogeneous Distance Learning Based on Kernel Analysis-Synthesis Dictionary for Semi-Supervised Image to Video Person Re-Identification

Bow image retrieval method based on SSD target detection

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