Hidden-layer ensemble fusion of MLP neural networks for pedestrian detection

Kyaw Kyaw Htike, “Hidden-layer ensemble fusion of MLP neural networks for pedestrian detection”, Informatica, Vol. 41, no. 1, pp. 87-97, 2017. [ISI (ESCI) and Scopus-indexed journal]


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.