Automatic detection of bike-riders without helmet using surveillance videos in real-time

Dahiya, K and Singh, D and C, Krishna Mohan (2016) Automatic detection of bike-riders without helmet using surveillance videos in real-time. In: International Joint Conference on Neural Networks (IJCNN), 24-29 July 2016.

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Abstract

In this paper, we propose an approach for automatic detection of bike-riders without helmet using surveillance videos in real time. The proposed approach first detects bike riders from surveillance video using background subtraction and object segmentation. Then it determines whether bike-rider is using a helmet or not using visual features and binary classifier. Also, we present a consolidation approach for violation reporting which helps in improving reliability of the proposed approach. In order to evaluate our approach, we have provided a performance comparison of three widely used feature representations namely histogram of oriented gradients (HOG), scale-invariant feature transform (SIFT), and local binary patterns (LBP) for classification. The experimental results show detection accuracy of 93.80% on the real world surveillance data. It has also been shown that proposed approach is computationally less expensive and performs in real-time with a processing time of 11.58 ms per frame.

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IITH Creators:
IITH CreatorsORCiD
C, Krishna MohanUNSPECIFIED
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Real-time systems, Feature extraction, Surveillance, Videos, Visualization, Cameras, Transforms
Subjects: Computer science > Special computer methods
Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 13 Jan 2017 11:43
Last Modified: 01 Sep 2017 09:14
URI: http://raiithold.iith.ac.in/id/eprint/2987
Publisher URL: https://doi.org/10.1109/IJCNN.2016.7727586
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