TY - GEN
T1 - Speeding up HOG and LBP features for pedestrian detection by multiresolution techniques
AU - Geismann, Philip
AU - Knoll, Alois
PY - 2010
Y1 - 2010
N2 - In this article, we present a fast pedestrian detection system for driving assistance. We use current state-of-the-art HOG and LBP features and combine them into a set of powerful classifiers. We propose an encoding scheme that enables LBP to be used efficiently with the integral image approach. This way, HOG and LBP block features can be computed in constant time, regardless of block position or scale. To further speed up the detection process, a coarse-to-fine scanning strategy based on input resolution is employed. The original camera resolution is consecutively downsampled and fed to different stage classifiers. Early stages in low resolutions reject most of the negative candidate regions, while few samples are passed through all stages and are evaluated by more complex features. Results presented on the INRIA set show competetive accuracy performance, while both processing and training time of our system outperforms current state-of-the-art work.
AB - In this article, we present a fast pedestrian detection system for driving assistance. We use current state-of-the-art HOG and LBP features and combine them into a set of powerful classifiers. We propose an encoding scheme that enables LBP to be used efficiently with the integral image approach. This way, HOG and LBP block features can be computed in constant time, regardless of block position or scale. To further speed up the detection process, a coarse-to-fine scanning strategy based on input resolution is employed. The original camera resolution is consecutively downsampled and fed to different stage classifiers. Early stages in low resolutions reject most of the negative candidate regions, while few samples are passed through all stages and are evaluated by more complex features. Results presented on the INRIA set show competetive accuracy performance, while both processing and training time of our system outperforms current state-of-the-art work.
UR - http://www.scopus.com/inward/record.url?scp=78650766551&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17289-2_24
DO - 10.1007/978-3-642-17289-2_24
M3 - Conference contribution
AN - SCOPUS:78650766551
SN - 3642172881
SN - 9783642172885
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 243
EP - 252
BT - Advances in Visual Computing - 6th International Symposium, ISVC 2010, Proceedings
T2 - 6th International, Symposium on Visual Computing, ISVC 2010
Y2 - 29 November 2010 through 1 December 2010
ER -