Pedestrian Detection Based on Sparse and Low-Rank Matrix Decomposition

Cheng Ke-yang, Mao Qi-rong, Zhan Yong-zhao

Abstract


This article puts forward a novel system for pedestrian detection tasks, which proposing a model with sparse and low-rank matrix decomposition, jointly alternating direction method to solve the convex relaxation problem. We present an efficient pedestrian detection system using mixing features with sparse and low-rank matrix decomposition to combine into a Kernel classifier. Results presented on our data set show competitive accuracy and robust performance of our system outperforms current state-of-the-art work.

 

DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.3859


Keywords


pedestrian detection ,Matrix decomposition, sparse, low-rank, alternating direction method

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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