Estimating the spatial-temporal distribution of urban street ponding levels from surveillance videos based on computer vision

Hao, Xin; Lyu, Heng; Wang, Ze; Fu, Shengnan; Zhang, Chi

doi:10.1007/s11269-022-03107-2

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…With the continuous development of the economy, various large-scale transportation hubs and various public places have appeared, and the passenger flow has caused great pressure on the transportation hubs and public places; if passenger flow statistics are carried out for commercial places, operators can make scientific and effective decisions through the data of passenger flow statistics, thereby increasing the profits of operators. Counting the flow of people in cultural and entertainment places such as scenic spots can count the changes in the flow of people in real time and then get the trend of off-season and peak season, which is convenient to establish a safety warning mechanism [4]. Real-time scheduling and management can be carried out through passenger flow statistics of subway stations, airports, and other transportation hubs.…”

Section: Introductionmentioning

confidence: 99%

Analysis of People Flow Image Detection System Based on Computer Vision Sensor

Yang

Wang

2022

Journal of Sensors

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In order to meet the needs of accurately grasping the situation of people in the mall at all times, the author proposes an analysis method based on computer vision for people flow image detection system. This method combines the HOG feature with the SVM classifier, detects pedestrians through dual cameras, and builds an experimental research platform for dual-camera joint detection of pedestrians. The result shows that the error rate of human flow detected by the author’s method is the lowest of 0% and the highest of 6.25%. Conclusion. This method has a good effect on the statistics of the number of people in the shopping mall and can reduce the workload of the monitoring personnel in the shopping mall.

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Section: Introductionmentioning

confidence: 99%

Analysis of People Flow Image Detection System Based on Computer Vision Sensor

Yang

Wang

2022

Journal of Sensors

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“…Typically, temporal studies involving image data use images (or video) from fixed locations. This data is used to do things such as evaluate disaster recovery [15], monitor ecological change [16], or measure urban flooding [17]. Data from fixed cameras is also used to count people [18].…”

Section: Introductionmentioning

confidence: 99%

Open-source data pipeline for street-view images: A case study on community mobility during COVID-19 pandemic

Martell,

Terry,

Sengupta

et al. 2024

PLoS ONE

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Street View Images (SVI) are a common source of valuable data for researchers. Researchers have used SVI data for estimating pedestrian volumes, demographic surveillance, and to better understand built and natural environments in cityscapes. However, the most common source of publicly available SVI data is Google Street View. Google Street View images are collected infrequently, making temporal analysis challenging, especially in low population density areas. Our main contribution is the development of an open-source data pipeline for processing 360-degree video recorded from a car-mounted camera. The video data is used to generate SVIs, which then can be used as an input for longitudinal analysis. We demonstrate the use of the pipeline by collecting an SVI dataset over a 38-month longitudinal survey of Seattle, WA, USA during the COVID-19 pandemic. The output of our pipeline is validated through statistical analyses of pedestrian traffic in the images. We confirm known results in the literature and provide new insights into outdoor pedestrian traffic patterns. This study demonstrates the feasibility and value of collecting and using SVI for research purposes beyond what is possible with currently available SVI data. Our methods and dataset represent a first of its kind longitudinal collection and application of SVI data for research purposes. Limitations and future improvements to the data pipeline and case study are also discussed.

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