Prof. Shengke Wang Profile

Prof. Shengke Wang

at Ocean Univ of China

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Publications (7)

Proceedings Article | 27 June 2023 Paper

Crowded people detection for occluded classroom surveillance scenes based on relation model

Yuping Li, Fengqin Yao, Xuan Guo, Shengke Wang

Proceedings Volume 12705, 127052L (2023) https://doi.org/10.1117/12.2680205

KEYWORDS: Object detection, Bismuth, Video surveillance, Surveillance, Education and training, Ablation, Model-based design, Feature extraction, Modeling, Convolution

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Proceedings Article | 27 June 2023 Paper

Person re-identification with IBN layer and channel attention module for indoor scenarios

Hao Wang, Guoan Cheng, Yongdong Li, Guiyan Cai, Lu Sun, Shengke Wang

Proceedings Volume 12705, 127050I (2023) https://doi.org/10.1117/12.2680593

KEYWORDS: Feature extraction, Surveillance, Education and training, Batch normalization, Machine learning, Data modeling, Cameras, Image segmentation, Deep learning, Reflection

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Person re-identification technology is being utilized increasingly frequently in autonomous processing and analysis of surveillance video jobs as a result of recent advancements in deep learning, particularly with safety precautions and smart transportation. As a result of the issues with inadequate illumination and reflection in indoor settings, At the moment, much of the related research on human re-identification concentrates on outside situations, with little attention paid to indoor scenarios. These make the process of person re-identification in complicated indoor scenarios very difficult. The indoor person re-identification algorithm is investigated in this research in order to increase the precision of person recognition in indoor settings. The IBN layer is an addition to the Resnet50 backbone network that uses a combination of instance normalization (IN) and batch normalization (BN) to eliminate individual appearance difference while retaining the feature difference of different individuals to address the issues with the obvious difference of light and shade in person images taken by indoor monitoring. To enhance the expressiveness capability of individual features, the attention module based on feature channel is added to the residual network. In specifically, the learning approach automatically determines the value of each channel in a person's attributes in order to amplify the important qualities and suppress the unnecessary ones. On the other hand, to address the issue of it being challenging to distinguish between similar people caused by more interference factors such as occlusion and reflection in indoor scene, we introduce triple loss in the model training process, which can make the model better learn the details of persons. The three primary validation data sets utilized in this study are Market1501, OUC365, and DukeMTMC-reID. The indoor style and high definition in the OUC365 data set are more obvious, the noise is more obvious in the Market1501 data set, and there is a significant difference in the number of photos among various people in the DukeMTMC-reID data set. The proposed method is tested on several data sets in this paper, and successful results are obtained.

Proceedings Article | 27 January 2021 Paper

Panoptic segmentation of UAV images with deformable convolution network and mask scoring

Hongwei Chen, Laihui Ding, Fengqin Yao, Pengfei Ren, Shengke Wang

Proceedings Volume 11720, 1172013 (2021) https://doi.org/10.1117/12.2589337

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Proceedings Article | 6 May 2019 Paper

Person re-identification for 365-day video surveillance based on stride convolutional neural network

Shengke Wang, Xiaoyan Zhang, Rui Li, Jianlin Zhu, Fenghui Xue, Junyu Dong

Proceedings Volume 11069, 110690N (2019) https://doi.org/10.1117/12.2524371

KEYWORDS: Convolutional neural networks, Convolution, Cameras, Video surveillance, Lithium, Data modeling, Expectation maximization algorithms, Light sources and illumination, Image processing, Image fusion

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Proceedings Article | 6 May 2019 Paper

A multiple people tracking algorithm for large view scene

Shengke Wang, Yuan Cao, Guoan Cheng, Fenghui Xue, Junyu Dong

Proceedings Volume 11069, 1106943 (2019) https://doi.org/10.1117/12.2524205

KEYWORDS: Switches, Video, Sensors, Detection and tracking algorithms, Motion models, Feature extraction, Data processing, Video surveillance, Unmanned aerial vehicles, Image processing

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Proceedings Article | 10 April 2018 Paper

Small-size pedestrian detection in large scene based on fast R-CNN

Shengke Wang, Na Yang, Lianghua Duan, Lu Liu, Junyu Dong

Proceedings Volume 10615, 106150J (2018) https://doi.org/10.1117/12.2305889

KEYWORDS: Sensors, Data modeling, Performance modeling, Sodium, Lutetium, Information science, Video surveillance, Target detection, Cameras, Detector development

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SPIE Journal Paper | 11 January 2018

Synthetic aperture radar image change detection based on frequency-domain analysis and random multigraphs

Feng Gao, Xiao Wang, Junyu Dong, Shengke Wang

JARS, Vol. 12, Issue 01, 016010, (January 2018) https://doi.org/10.1117/12.10.1117/1.JRS.12.016010

KEYWORDS: Synthetic aperture radar, Speckle, Computer simulations, Image classification, Visualization, Image sensors, Image processing, Quantitative analysis, Image analysis, Sensors

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Showing 5 of 7 publications

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