Publication
Boosted random ferns for object detection
Journal Article (2018)
Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pages
272-288
Volume
40
Number
2
Doc link
http://dx.doi.org/10.1109/TPAMI.2017.2676778
File
Authors
Projects associated
2014 SGR 897: Grup de Recerca Consolidat - VIS
AEROARMS: AErial RObotics System integrating multiple ARMS and advanced manipulation capabilities for inspection and maintenance
RobInstruct: Instructing robots using natural communication skills
LOGIMATIC: Tight integration of EGNSS and on-board sensors for port vehicle automation
ColRobTransp: Colaboración robots-humanos para el transporte de productos en zonas urbanas
Abstract
In this paper we introduce the Boosted Random Ferns (BRFs) to rapidly build discriminative classifiers for learning and detecting object categories. At the core of our approach we use standard random ferns, but we introduce four main innovations that let us bring ferns from an instance to a category level, and still retain efficiency. First, we define binary features on the histogram of oriented gradients-domain (as opposed to intensity-), allowing for a better representation of intra-class variability. Second, both the positions where ferns are evaluated within the sliding window, and the location of the binary features for each fern are not chosen completely at random, but instead we use a boosting strategy to pick the most discriminative combination of them. This is further enhanced by our third contribution, that is to adapt the boosting strategy to enable sharing of binary features among different ferns, yielding high recognition rates at a low computational cost. And finally, we show that training can be performed online, for sequentially arriving images. Overall, the resulting classifier can be very efficiently trained, densely evaluated for all image locations in about 0.1 seconds, and provides detection rates similar to competing approaches that require expensive and significantly slower processing times. We demonstrate the effectiveness of our approach by thorough experimentation in publicly available datasets in which we compare against state-of-the-art, and for tasks of both 2D detection and 3D multi-view estimation.
Categories
computer vision, object detection.
Author keywords
online-boosting, Image processing and computer vision, object detection, random ferns, boosting
Scientific reference
M. Villamizar, J. Andrade-Cetto, A. Sanfeliu and F. Moreno-Noguer. Boosted random ferns for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(2): 272-288, 2018.
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