Abstract
People detection has always posed a challenge in the image processing domain, and a lot of research has been going on to solve the problems posed due to the large number of variants present in detecting a human. In this paper, we introduce a system that integrates various components to perform pedestrian detection, safe distance calculation and a risk assessment for pedestrians, with a warning if the pedestrian is potentially in danger. The proposed system is for middle income cars, where a web camera is connected to a Raspberry Pi 3. The processor performs the calculations based on live webcam feed and gives an alert to the driver via an Android Application. This project uses a custom LBP Cascade classifier written in OpenCV, for pedestrian detection. A comparison of results between this custom classifier with the standard Haar classifier is shown here. This system is tested on an Indian dataset collected by us and on the Penn-Fudan Database for Pedestrian Detection and Segmentation dataset for comparing the results.
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Acknowledgment
We would like to thank Mr. Alessandro Ferrari and his team at ARGO Vision for sharing his LBP classifier with us for the purpose of this project. We would also like to thank them for making their resources available for students and professionals. Using this file has been crucial to our system’s successful implementation.
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Mehta, M., Gupta, R. (2019). LBP-Haar Cascade Based Real-Time Pedestrian Protection System Using Raspberry Pi. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1035. Springer, Singapore. https://doi.org/10.1007/978-981-13-9181-1_6
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DOI: https://doi.org/10.1007/978-981-13-9181-1_6
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