Object recognition using the OpenCV Haar cascade-classifier on the iOS platform
Augmented reality (AR), the compiling of layered computer-generated information to real-time stream data, has recently become a buzzword in the mobile application communities, as real-time vision computing has become more and more feasible. Hardware advances have allowed numerous such utility and game applications to be deployed to mobile devices. This report presents a high-level implementation of live object recognition of automobile interiors, using Open Source Computer Vision Library (OpenCV) on the iOS platform. Two mobile devices where used for image processing: an iPhone 3GS and an iPhone 4. A handful of key-feature matching technics and one supervised learning classification approach were considered for this implementation. Speeded Up Robust Features (SURF) detection (a key-feature matching technique) and Haar classification (supervised learning approach) were implemented, and Haar classification was used in the final AR prototype. Although the object classifiers are not yet to satisfaction in terms of accuracy, a problem that could be overcome by more extensive training, the implementation performs sufficiently in terms of speed for the purpose of this AR prototype.
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