Abstract:

We introduce a framework for evaluating human detectors that considers the practical application of a detector on a full image using multi-size sliding window scanning. We produce DET (Detection Error Tradeoff) curves relating miss detection rate and false alarm rate computed by deploying the detector on cropped windows as well as whole images, using in the later either image resize or feature resize. Plots for cascade classifiers are generated based on confidence scores instead of varying the number of layers. To assess a method's overall performance on a given test, we use the ALMR (Average Log Miss Rate) 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, as well as 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 of features can have serious consequences.