Part-based rdf for direction classification of pedestrians, and a benchmark


by J Tao, R Klette
Abstract:
This paper proposes a new benchmark dataset for pedestrian body-direction classification, proposes a new framework for intra-class classification by directly aiming at pedestrian body-direction classification, shows that the proposed framework outperforms a state-of-the-art method,and it also proposes the use of DCT-HOG features (by combining a discrete cosine transform with the histogram of oriented gradients) as a novel approach for defining a random decision forest.
Reference:
Part-based rdf for direction classification of pedestrians, and a benchmark (J Tao, R Klette), In Computer Vision – ACCV 2014 Workshops, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, volume 9009, 2015.
Bibtex Entry:
@inproceedings{tao2015part-basedbenchmark,
author = "Tao, J and Klette, R",
booktitle = "Computer Vision - ACCV 2014 Workshops, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
organization = "Singapore",
pages = "418--432",
publisher = "Springer Verlag",
title = "Part-based rdf for direction classification of pedestrians, and a benchmark",
volume = "9009",
year = "2015",
abstract = "This paper proposes a new benchmark dataset for pedestrian body-direction classification, proposes a new framework for intra-class classification by directly aiming at pedestrian body-direction classification, shows that the proposed framework outperforms a state-of-the-art method,and it also proposes the use of DCT-HOG features (by combining a discrete cosine transform with the histogram of oriented gradients) as a novel approach for defining a random decision forest.",
doi = "10.1007/978-3-319-16631-5_31",
startyear = "2014",
startmonth = "Nov",
startday = "1",
finishyear = "2014",
finishmonth = "Nov",
finishday = "2",
isbn = "9783319166308",
issn = "0302-9743",
eissn = "1611-3349",
language = "eng",
conference = "Computer Vision - ACCV 2014 Workshops",
}