BCN Perceptual Computing Lab Repository

Real-time object detection using an evolutionary boosting strategy

Baró, Xavier and Vitrià, Jordi Real-time object detection using an evolutionary boosting strategy. Artificial Intelligence Resarch and Development, Frontiers in Artificial Intelligence and Applications .

PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader


This paper presents a brief introduction to the nowadays most used object detection scheme. From this scheme, we highlight the two critical points of this scheme in terms of training time, and present a variant of this scheme that solves one of these points. Our proposal is to replace the WeakLearner in the Adaboost algorithm by a genetic algorithm. In addition, this approach allows us to work with high dimensional feature spaces which can not be used in the traditional scheme. In this paper we also use the dissociated dipoles, a generalized version of the Haarlike features used on the detection scheme. This type of features is an example of high dimensional feature space, moreover, when we extend it to color spaces.

Item Type:Article
Subjects:Q Science > QA Mathematics > QA76 Computer software
ID Code:32
Deposited By:n/d Generic User
Deposited On:03 Aug 2009 20:42
Last Modified:29 Nov 2010 17:52

Repository Staff Only: item control page