Object recognition is a well studied but extremely challenging field. Human detection is an especially important part of object recognition as it has played a role in machine and human interaction, biometrics, unmanned vehicles, as well as tracking and surveillance. We first present a hardware implementation of the successful Histograms of Oriented Gradients (HOG) method for human detection. The implementation significantly speeds up the method achieving 38 frames a second on VGA video while testing 11,160 sliding windows per frame. The accuracy remains comparable to the CPU implementation. Analysis of the HOG method and other popular object recognition methods led to a novel approach for object detection using a feature construction method called Evolution-COnstructed (ECO) features. Most other approaches rely on human experts to construct features for object recognition. ECO features are automatically constructed by uniquely employing a standard genetic algorithm to discover series of transforms that are highly discriminative. Using ECO features provides several advantages over other object detection algorithms including: no need for a human expert to build feature sets or tune their parameters, ability to generate specialized feature sets for different objects, and no limitations to certain types of image sources. We show in our experiments that ECO features perform better or comparable with state-of-the-art object recognition algorithms making it the first feature construction method to compete with features created by human experts at general object recognition. An analysis is given of ECO features which includes a visualization of ECO features and improvements made to the algorithm.
College and Department
Ira A. Fulton College of Engineering and Technology; Electrical and Computer Engineering
BYU ScholarsArchive Citation
Lillywhite, Kirt D., "Feature Construction Using Evolution-COnstructed Features for General Object Recognition" (2012). Theses and Dissertations. 2974.
ECO features, object detection, feature construction, genetic algorithm, self-tuned, AdaBoost