Paper Info
Title
Efficient Detection For Spatially Local Coding
Abstract
In this paper, we present an efficient detector for the Spatially Local Coding (SLC) object model. SLC is a recent, high performing object classifier that has yet to be applied in a detection (object localization) setting. SLC uses features that jointly code for both appearance and location, making it difficult to apply the existing approaches to efficient detection. We design an approximate Hough transform for the SLC model that uses a cascade of thresholds followed by gradient descent to achieve efficiency as well as accurate localization. We evaluate the resulting detector on the Daimler Monocular Pedestrian dataset.
Year
DOI
Venue
2014
10.1007/978-3-319-16628-5_44
COMPUTER VISION - ACCV 2014 WORKSHOPS, PT I
Field
DocType
Volume
Computer vision,Gradient descent,Pattern recognition,Computer science,Hough transform,Object model,Coding (social sciences),Artificial intelligence,Cascade,Monocular,Classifier (linguistics),Detector
Conference
9008
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
17
2
Name
Order
Citations
PageRank
Sancho McCann12007.28
D. G. Lowe2157181413.60