A Comprehensive Evaluation Framework and a Comparative Study for Human Detectors. Hussein, M., Porikli, F., & Davis, L. 10(3):417–427, 2009. doi abstract bibtex We introduce a framework for evaluating human detectors that considers the practical application of a detector on a full image using multisize sliding-window scanning. We produce detection error tradeoff (DET) curves relating the miss detection rate and the false-alarm rate computed by deploying the detector on cropped windows and whole images, using, in the latter, either image resize or feature resize. Plots for cascade classifiers are generated based on confidence scores instead of on variation of the number of layers. To assess a method's overall performance on a given test, we use the average log miss rate (ALMR) as an aggregate performance score. To analyze the significance of the obtained results, we conduct 10-fold cross-validation experiments. We applied our evaluation framework to two state-of-the-art cascade-based detectors on the standard INRIA person dataset and a local dataset of near-infrared images. We used our evaluation framework to study the differences between the two detectors on the two datasets with different evaluation methods. Our results show the utility of our framework. They also suggest that the descriptors used to represent features and the training window size are more important in predicting the detection performance than the nature of the imaging process, and that the choice between resizing images or features can have serious consequences.
@article{hussein_comprehensive_2009,
title = {A Comprehensive Evaluation Framework and a Comparative Study for Human Detectors},
volume = {10},
rights = {All rights reserved},
issn = {1524-9050},
doi = {10.1109/TITS.2009.2026670},
abstract = {We introduce a framework for evaluating human detectors that considers the practical application of a detector on a full image using multisize sliding-window scanning. We produce detection error tradeoff ({DET}) curves relating the miss detection rate and the false-alarm rate computed by deploying the detector on cropped windows and whole images, using, in the latter, either image resize or feature resize. Plots for cascade classifiers are generated based on confidence scores instead of on variation of the number of layers. To assess a method's overall performance on a given test, we use the average log miss rate ({ALMR}) as an aggregate performance score. To analyze the significance of the obtained results, we conduct 10-fold cross-validation experiments. We applied our evaluation framework to two state-of-the-art cascade-based detectors on the standard {INRIA} person dataset and a local dataset of near-infrared images. We used our evaluation framework to study the differences between the two detectors on the two datasets with different evaluation methods. Our results show the utility of our framework. They also suggest that the descriptors used to represent features and the training window size are more important in predicting the detection performance than the nature of the imaging process, and that the choice between resizing images or features can have serious consequences.},
pages = {417--427},
number = {3},
journaltitle = {{IEEE} Transactions on Intelligent Transportation Systems},
author = {Hussein, M. and Porikli, F. and Davis, L.},
date = {2009-09},
year = {2009},
keywords = {Aggregates, average log miss rate, Cascade, cascade classifier, comprehensive evaluation framework, cropped window, {DET} curve, detection error tradeoff, evaluation, false-alarm rate, feature resize, Histograms, histograms of oriented gradients ({HOGs}), human detection, human detector, Humans, image classification, image resize, Infrared detectors, infrared imaging, {INRIA} person dataset, Intelligent transportation systems, Intelligent vehicles, Laboratories, miss detection rate, multisize sliding-window scanning, near infrared, near-infrared image, object detection, Optical imaging, region covariance, Testing, Vehicle detection},
file = {IEEE Xplore Abstract Record:C\:\\Users\\Mohamed Hussein\\Zotero\\storage\\EUYURI2J\\5169850.html:text/html}
}
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To assess a method's overall performance on a given test, we use the average log miss rate (ALMR) as an aggregate performance score. To analyze the significance of the obtained results, we conduct 10-fold cross-validation experiments. We applied our evaluation framework to two state-of-the-art cascade-based detectors on the standard INRIA person dataset and a local dataset of near-infrared images. We used our evaluation framework to study the differences between the two detectors on the two datasets with different evaluation methods. Our results show the utility of our framework. 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