Hidden-layer Ensemble Fusion of MLP Neural Networks for Pedestrian Detection

Authors

  • Kyaw Kyaw Htike UCSI University

Abstract

Being able to detect pedestrians is a crucial task for intelligent agents especially for autonomous vehicles, robots navigating in cities, machine vision, automatic traffic control in smart cities, and public safety and security. Various sophisticated pedestrian detection systems have been presented in literature and most of the state-of-the-art systems have two main components: feature extraction and classification. Over the past decade, the majority of the attention has been paid to feature extraction. In this paper, we show that much can be gained by having a high-performing classification algorithm, and changing only the classification component of the detection pipeline while fixing the feature extraction mechanism constant, we show reduction in pedestrian detection error (in terms of log-average miss rate) by over 40%. To be specific, we propose a novel algorithm for generating a compact and efficient ensemble of Multi-layer Perceptron neural networks that is well-suited for pedestrian detection both in terms of detection accuracy and speed. We demonstrate the efficacy of our proposed method by comparing with several state-of-the-art pedestrian detection algorithms.

Author Biography

Kyaw Kyaw Htike, UCSI University

Kyaw Kyaw Htike received his Ph.D. in Computer Vision and Machine Learning from University of Leeds, UK. His thesis was on developing novel algorithms for domain adaptation applied to pedestrian detection. He spent a year working as a Research Fellow at University of Leeds on the project "Learning to Recognise Dynamic Visual Content from Broadcast Footage" in collaboration with University of Oxford and University of Surrey. His research interests include Computer Vision, Machine Learning, object detection, feature extraction, and integrating different supervision levels in the machine learning process to solve image recognition problems. He is currently an Assistant Professor at the School of Information Technology, UCSI University, Malaysia.

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Published

2017-01-24

How to Cite

Htike, K. K. (2017). Hidden-layer Ensemble Fusion of MLP Neural Networks for Pedestrian Detection. Informatica, 41(1). Retrieved from https://puffbird.ijs.si/index.php/informatica/article/view/1397

Issue

Vol. 41 No. 1 (2017)

Section

Technical papers

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