On comprehensive analysis of learning algorithms on pedestrian detection using shape features

Article type: Research Article

Authors: Ardiyanto, Igi^{; *} | Adji, Teguh Bharata | Asmaraman, Dika Akilla

Affiliations: Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia

Correspondence: [*] Corresponding author. Igi Ardiyanto, Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Jl. Grafika No. 2, Yogyakarta, Indonesia. E-mail: igi@ugm.ac.id.

Abstract: Despite the surge of deep learning, deploying the deep learning-based pedestrian detection into the real system faces hurdles, mainly due to the huge resource usages. The classical feature-based detection system still becomes feasible option. There have been many efforts to improve the performance of pedestrian detection system. Among many feature set, Histogram of Oriented Gradient seems to be very effective for person detection. In this research, various machine learning algorithms are investigated for person detection. Different machine learning algorithms are evaluated to obtain the optimal accuracy and speed of the system.

Keywords: Pedestrian detection, machine learning, Histogram of Oriented Gradient, shape features

DOI: 10.3233/JIFS-18491

Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 4, pp. 4807-4820, 2018