L1 norm based pedestrian detection using video analytics technique

Selvaraj, Anandamurugan, Selvaraj, Jeeva ORCID logoORCID: https://orcid.org/0000-0002-1029-0879, Maruthaiappan, Sivabalakrishnan, Babu, Gokulnath Chandra and Kumar, Priyan Malarvizhi ORCID logoORCID: https://orcid.org/0000-0001-6149-2705 (2020) L1 norm based pedestrian detection using video analytics technique. Computational Intelligence, 36 (4) . pp. 1569-1579. ISSN 0824-7935 [Article] (doi:10.1111/coin.12292)


Pedestrian detection from images of the visible spectrum is a high relevant area of research given its potential impact in the design of pedestrian protection systems. In general, detection is made with two different phases, feature extraction and classification. Also, features for detection of pedestrian are already are available such as optimal feature model. But still required is an improvement in detection by reducing the execution time and false positive. The proposed model has three different phases, that is, background subtraction, feature extraction, and classification. In spite of giving entire information into feature extraction, the system gives only a useful information (foreground image) by twin background model. Then the foreground image moves to the feature extraction and classifies the pedestrian. For feature extraction, histogram of orientation gradient (HOG) L1 normalization has been used. This will increase the detection accuracy and reduce the computation time of a process. In addition, false positive rate has been minimized.

Item Type: Article
Keywords (uncontrolled): Artificial Intelligence, Computational Mathematics
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 31488
Useful Links:
Depositing User: Jisc Publications Router
Date Deposited: 09 Dec 2020 09:35
Last Modified: 23 Jun 2022 23:14
URI: https://eprints.mdx.ac.uk/id/eprint/31488

Actions (login required)

View Item View Item


Activity Overview
6 month trend
6 month trend

Additional statistics are available via IRStats2.