Cross-Scene Counting Based on Domain Adaptation-Extreme Learning Machine

Yang, Biao; Cao, Jinmeng; Wang, Nan; Zhang, Yuyu; Cui, Guozeng

doi:10.1109/access.2018.2800688

Cited by 12 publications

(3 citation statements)

References 28 publications

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“…Because of this problem, many models on crowd analysis are scene-specific and would need to be retrained with new data to fit a different scene. Efforts are underway to enable scene-independent crowd analyses (Shao, Change Loy and Wang 2014; Yang, Cao, Wang et al 2018), which would widen the application possibilities.…”

Section: Looking Forwardmentioning

confidence: 99%

Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing

Bernasco

Hoeben

Koelma

et al. 2022

Sociological Methods & Research

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Social scientists increasingly use video data, but large-scale analysis of its content is often constrained by scarce manual coding resources. Upscaling may be possible with the application of automated coding procedures, which are being developed in the field of computer vision. Here, we introduce computer vision to social scientists, review the state-of-the-art in relevant subfields, and provide a working example of how computer vision can be applied in empirical sociological work. Our application involves defining a ground truth by human coders, developing an algorithm for automated coding, testing the performance of the algorithm against the ground truth, and running the algorithm on a large-scale dataset of CCTV images. The working example concerns monitoring social distancing behavior in public space over more than a year of the COVID-19 pandemic. Finally, we discuss prospects for the use of computer vision in empirical social science research and address technical and ethical challenges.

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Section: Looking Forwardmentioning

confidence: 99%

Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing

Bernasco

Hoeben

Koelma

et al. 2022

Sociological Methods & Research

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show abstract

“…In [22], they propose a Fully Convolutional Neural Network(FCN) and a weighted adaptive human Gaussian model for person detection and then apply it to the new scene with few labeled data. DA-ELM [39], a counting model based on domain adaptationextreme learning machine, counts the people in a new scene with only a half of the training samples compared with counting without domain adaptation. In [14], they propose a one-shot learning approach for learning how to adapt to a target scene using one labeled example.…”

Section: Related Workmentioning

confidence: 99%

A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View

Zhao

Han

et al. 2020

Preprint

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Drones shooting can be applied in dynamic traffic monitoring, object detecting and tracking, and other vision tasks. The variability of the shooting location adds some intractable challenges to these missions, such as varying scale, unstable exposure, and scene migration. In this paper, we strive to tackle the above challenges and automatically understand the crowd from the visual data collected from drones. First, to alleviate the background noise generated in cross-scene testing, a double-stream crowd counting model is proposed, which extracts optical flow and frame difference information as an additional branch. Besides, to improve the model's generalization ability at different scales and time, we randomly combine a variety of data transformation methods to simulate some unseen environments. To tackle the crowd density estimation problem under extreme dark environments, we introduce synthetic data generated by game Grand Theft Auto V(GTAV). Experiment results show the effectiveness of the virtual data. Our method wins the challenge with a mean absolute error (MAE) of 12.70 1 . Moreover, a comprehensive ablation study is conducted to explore each component's contribution.

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“…For the crowd counting task, researchers have proposed various approaches, such as detection-based counting [9], regression-based counting [10] and density estimation-based counting [6]. Comparing to detection-based approach and regression-based approach, the density estimation-based approach can provide more spatial information of the crowd distribution, which can be used as the effective cues for the crowd analysis [11].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Scale-Adaptive Approach With Attention Mechanism-Based Crowd Spatial Information for Crowd Counting

Kong

Xing

et al. 2019

IEEE Access

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This paper proposes an automatic scale-adaptive approach with attention mechanism-based crowd spatial information addressing the crowd counting task, i.e. a novel cascaded crowd counting network. The proposed network is composed of a classification sub-network to estimate crowd scales and the main network to predict the corresponding density maps. First, the image serves as the input of the classification network and the main network. Second, according to the estimated crowd scale results, the main network structure is adjusted; simultaneously, the feature in the intermediate layer of the classification network is added stepwise into the main crowd counting network. Then, the semantic feature of the classification network is converted into the crowd spatial information mask via the proposed spatial attention conversion module, and the crowd spatial information mask is weighted into the specific feature of the main network. Last, the crowd density map and the crowd counting result are obtained. The experiments on challenging Mall, the Shanghaitech_A and Shanghaitech_B datasets prove the effectiveness, feasibility, and robustness of the proposed method, and the ablation study demonstrates the structure rationality of the proposed network.

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Cross-Scene Counting Based on Domain Adaptation-Extreme Learning Machine

Cited by 12 publications

References 28 publications

Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing

Promise Into Practice: Application of Computer Vision in Empirical Research on Social Distancing

A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View

An Automatic Scale-Adaptive Approach With Attention Mechanism-Based Crowd Spatial Information for Crowd Counting

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