Title :
Introspective classification for pedestrian detection
Author :
Blair, Calum G. ; Thompson, John ; Robertson, Neil M.
Author_Institution :
Inst. for Digital Commun., Univ. of Edinburgh, Edinburgh, UK
Abstract :
State-of-the-art pedestrian detectors are capable of finding humans in images with reasonable accuracy. However, accurate object detectors such as Integral Channel Features (ICF) do not provide good reliability; they are unable to identify detections which they are less confident (or more uncertain) about. We apply existing methods for generating probabilistic measures from classifier scores (such as Piatt exponential scaling and Isotonic Regression) and compare these to Gaussian Process classifiers (GPCs), which can provide more informative predictive variance. GPCs are less accurate than ICF classifiers, but GPCs and Adaboost with Piatt scaling both provide improved reliability over existing methods.
Keywords :
image classification; object detection; pedestrians; probability; traffic engineering computing; Adaboost; GPCs; Gaussian process classifiers; ICF classifiers; Piatt exponential scaling; informative predictive variance; integral channel features; introspective classification; isotonic regression; object detectors; pedestrian detection; probabilistic measures; reliability; Entropy; MATLAB; Reliability; Visualization;
Conference_Titel :
Sensor Signal Processing for Defence (SSPD), 2014
Conference_Location :
Edinburgh
DOI :
10.1109/SSPD.2014.6943310