A Dynamic Part-Attention Model for Person Re-Identification

Yao, Ziying; Wu, Xinkai; Xiong, Zhongxia; Ma, Yalong

doi:10.3390/s19092080

Cited by 5 publications

(4 citation statements)

References 51 publications

(69 reference statements)

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“…Appearance based —Identifying people from their silhouettes can be approached as a re-identification (ReID) problem [ 18 , 19 , 20 ]. The vast majority of the literature on person ReID makes use of RGB images, as detailed in the review from Bedagkar-Gala et al [ 21 ] and the more recent deep-learning review from Wu et al [ 22 ].…”

Section: Related Workmentioning

confidence: 99%

Person Re-ID by Fusion of Video Silhouettes and Wearable Signals for Home Monitoring Applications

Masullo

Burghardt

Damen

et al. 2020

Sensors

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The use of visual sensors for monitoring people in their living environments is critical in processing more accurate health measurements, but their use is undermined by the issue of privacy. Silhouettes, generated from RGB video, can help towards alleviating the issue of privacy to some considerable degree. However, the use of silhouettes would make it rather complex to discriminate between different subjects, preventing a subject-tailored analysis of the data within a free-living, multi-occupancy home. This limitation can be overcome with a strategic fusion of sensors that involves wearable accelerometer devices, which can be used in conjunction with the silhouette video data, to match video clips to a specific patient being monitored. The proposed method simultaneously solves the problem of Person ReID using silhouettes and enables home monitoring systems to employ sensor fusion techniques for data analysis. We develop a multimodal deep-learning detection framework that maps short video clips and accelerations into a latent space where the Euclidean distance can be measured to match video and acceleration streams. We train our method on the SPHERE Calorie Dataset, for which we show an average area under the ROC curve of 76.3% and an assignment accuracy of 77.4%. In addition, we propose a novel triplet loss for which we demonstrate improving performances and convergence speed.

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Section: Related Workmentioning

confidence: 99%

Person Re-ID by Fusion of Video Silhouettes and Wearable Signals for Home Monitoring Applications

Masullo

Burghardt

Damen

et al. 2020

Sensors

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“…Most of the previously published works directly learn the feature representations from the whole pedestrian image, that contains background clutter. Quite recently, several person ReID deep learning-based systems have suggested learning effective feature representations from the detected pedestrian body to reduce the background clutter and improve the robustness of the person ReID system [6][7][8]. This motivates us to develop an automated image segmentation algorithm to eliminate background noise interference issues and enhance the discriminability of the extracted feature representations, even for an incomplete person, which may contain information that is discriminatory and deserves attention.…”

Section: Introductionmentioning

confidence: 99%

ReID-DeePNet: A Hybrid Deep Learning System for Person Re-Identification

Mohammed

Al-Fahdawi

Al-Waisy³

et al. 2022

Mathematics

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Person re-identification has become an essential application within computer vision due to its ability to match the same person over non-overlapping cameras. However, it is a challenging task because of the broad view of cameras with a large number of pedestrians appearing with various poses. As a result, various approaches of supervised model learning have been utilized to locate and identify a person based on the given input. Nevertheless, several of these approaches perform worse than expected in retrieving the right person in real-time over multiple CCTVs/camera views. This is due to inaccurate segmentation of the person, leading to incorrect classification. This paper proposes an efficient and real-time person re-identification system, named ReID-DeePNet system. It is based on fusing the matching scores generated by two different deep learning models, convolutional neural network and deep belief network, to extract discriminative feature representations from the pedestrian image. Initially, a segmentation procedure was developed based on merging the advantages of the Mask R-CNN and GrabCut algorithm to tackle the adverse effects caused by background clutter. Afterward, the two different deep learning models extracted discriminative feature representations from the pedestrian segmented image, and their matching scores were fused to make the final decision. Several extensive experiments were conducted, using three large-scale and challenging person re-identification datasets: Market-1501, CUHK03, and P-DESTRE. The ReID-DeePNet system achieved new state-of-the-art Rank-1 and mAP values on these three challenging ReID datasets.

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“…2 We propose a deformable convolution base-offset initialization strategy towards a more aligned receptive field, and further improvement of detection performance by forcing the aspect ratio of the deformable convolution kernel close to the pedestrian aspect ratio. 3 Several experiments are carried out on two benchmark datasets (the Caltech-USA and the CityPersons) to demonstrate the effectiveness and generalization of the proposed ALR pattern in both anchor-free and anchor-based methods.…”

Section: Introductionmentioning

confidence: 99%

“…Pedestrian detection is a necessary prerequisite and key component of recent research hotspots (e.g., pedestrian reidentification [ 1 , 2 , 3 ], human pose estimation [ 4 ]), for these tasks it is necessary to detect all the existing pedestrians accurately from images or videos before they go to the next step. In engineering fields, pedestrian detection is also an urgent need in the Advanced Driving Assistance System (ADAS) to help to reduce the occurrence of people-vehicle collisions, or in smart buildings for air conditioner control and monitoring systems [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Detection by Novel Axis-Line Representation and Regression Pattern

Zhang

Liu

2021

Sensors

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The pattern of bounding box representation and regression has long been dominant in CNN-based pedestrian detectors. Despite the method’s success, it cannot accurately represent location, and introduces unnecessary background information, while pedestrian features are mainly located in axis-line areas. Other object representations, such as corner-pairs, are not easy to obtain by regression because the corners are far from the axis-line and are greatly affected by background features. In this paper, we propose a novel detection pattern, named Axis-line Representation and Regression (ALR), for pedestrian detection in road scenes. Specifically, we design a 3-d axis-line representation for pedestrians and use it as the regression target during network training. A line-box transformation method is also proposed to fit the widely used box-annotations. Meanwhile, we explore the influence of deformable convolution base-offset on detection performance and propose a base-offset initialization strategy to further promote the gain brought by ALR. Notably, the proposed ALR pattern can be introduced into both anchor-based and anchor-free frameworks. We validate the effectiveness of ALR on the Caltech-USA and CityPersons datasets. Experimental results show that our approach outperforms the baseline significantly through simple modifications and achieves competitive accuracy with other methods without bells and whistles.

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A Dynamic Part-Attention Model for Person Re-Identification

Cited by 5 publications

References 51 publications

Person Re-ID by Fusion of Video Silhouettes and Wearable Signals for Home Monitoring Applications

Person Re-ID by Fusion of Video Silhouettes and Wearable Signals for Home Monitoring Applications

ReID-DeePNet: A Hybrid Deep Learning System for Person Re-Identification

Pedestrian Detection by Novel Axis-Line Representation and Regression Pattern

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