Pedestrian Detection Based on Sparse and Low-Rank Matrix Decomposition

Cheng Ke-yang (Nanjing University of aeronautics & astronautics)
Mao Qi-rong (Jiangsu University)
Zhan Yong-zhao (Jiangsu University)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 12, No 2: February 2014

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