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Modelling and Simulation in Engineering
Volume 2015, Article ID 948960, 8 pages
Research Article

A Comparative Study of Multiple Object Detection Using Haar-Like Feature Selection and Local Binary Patterns in Several Platforms

1Sidi Mohammed Ben Abdellah University, Faculty of Science and Technology, Renewable Energy and Smart Systems Laboratory, BP 2202, 30000 Fez, Morocco
2Sidi Mohammed Ben Abdellah University, National School of Applied Sciences, Renewable Energy and Smart Systems Laboratory, BP 72, 30000 Fez, Morocco

Received 7 October 2015; Accepted 2 December 2015

Academic Editor: Aiguo Song

Copyright © 2015 Souhail Guennouni et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Object detection has been attracting much interest due to the wide spectrum of applications that use it. It has been driven by an increasing processing power available in software and hardware platforms. In this work we present a developed application for multiple objects detection based on OpenCV libraries. The complexity-related aspects that were considered in the object detection using cascade classifier are described. Furthermore, we discuss the profiling and porting of the application into an embedded platform and compare the results with those obtained on traditional platforms. The proposed application deals with real-time systems implementation and the results give a metric able to select where the cases of object detection applications may be more complex and where it may be simpler.