Mr. Shaoli Tian Profile

Shaoli Tian

at Chongqing Univ

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Publications (2)

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Proceedings Article | 27 May 2022 Paper

RA-MISNet: multilevel information sharing network based on residual attention for crowd counting

Jun Sang, Shaoli Tian, Jinghan Tan, Mohammad Alam, Yan Chen

Proceedings Volume 12101, 121010K (2022) https://doi.org/10.1117/12.2618271

KEYWORDS: Image segmentation, Feature extraction, Head, Data modeling, Network architectures, Convolution, Machine vision, Computer vision technology

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Crowd counting has been a popular research topic in the field of computer vision due to the variation of human head scales and the interference of background noise. Some existing methods use multi-level feature fusion to solve scale variation, but the problem of background noise interference may be more serious due to the involvement of shallow features in the feature fusion process. In this paper, we propose a Multilevel Information Sharing Network based on Residual Attention(RA-MISNet) to solve this problem. The RA-MISNet consists of a feature extraction component, an information sharing module and a residual attention density map estimator. On the basis of solving the multi-scale problem, the residual attention mechanism is adopted by our proposed method to refine the population distribution information in sharing features at all levels, which can reduce the interference of complex texture background on density map regression. Furthermore, owing to the severe label noise interference problem in high-density crowd areas, we design a Regional Multi-level Segmentation Loss (RMS Loss) to divide the multi-level density regions with different label noise rates in a single crowd image and apply the corresponding granularity supervision constraints for each density level region. Extensive experiments on three crowd counting datasets (ShanghaiTech, UCF CC 50, UCF-QNRF) demonstrate the effectiveness and superiority of the proposed methods.

Proceedings Article | 12 April 2021 Paper

MSNet: multi-scale network for crowd counting

Ying Shi, Jun Sang, Mohammad Alam, Xinyue Liu, Shaoli Tian

Proceedings Volume 11735, 117350M (2021) https://doi.org/10.1117/12.2592677

KEYWORDS: Visualization, Machine vision, Distortion, Convolution, Computer vision technology

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Nowadays, due to various challenges such as large-scale variation of population, mutual occlusion, perspective distortion and so on, crowd counting has gradually become a hot issue in computer vision. To address the large- scale variation exists in the images, in this paper, we propose a novel multi-scale network called MSNet which aims to maintain continuous variations and count the number of pedestrians accurately. While most state-of-the- arts multi-scale and multi-column networks aim to integrate the scale information of heads with different size, lots of researches still need to do to achieve continuous variations. In MSNet, specifically, the first ten layers of the visual geometry group network(VGG) are used as the backbone to extract the rough features of images and a multi-scale block is employed to maintain the scale information which contains several receptive kernels to obtain a better performance towards the difficulty of scale-variation. Inspired by the knowledge that using multiple small receptive field kernels to replace a single large receptive field will get a better performance, we utilize two dilated convolutions with the receptive field of 5 to replace the large kernel. Our MSNet has moderate increase in computation, and we evaluate our method on three benchmark datasets including ShanghaiTech (Part A: MAE=59.6, RMSE=96.1; Part B: MAE=7.5, RMSE=12.1), UCF-CC-50(MAE=207.9, RMSE=273.8) and UCF-QNRF(MAE=93, RMSE=158) to show the outperformance of our method.

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