PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Real-Time Deformable Detector
Karim Ali, Francois Fleuret, David Hasler and Pascal Fua
IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 34, Number 2, pp. 225-239, 2012.

Abstract

We propose a new learning strategy for object detection. The proposed scheme forgoes the need to train a collection of detectors dedicated to homogeneous families of poses, and instead learns a single classifier that has the inherent ability to deform based on the signal of interest. We train a detector with a standard AdaBoost procedure by using combinations of pose-indexed features and pose estimators. This allows the learning process to select and combine various estimates of the pose with features able to compensate for variations in pose without the need to label data for training or explore the pose space in testing. We validate our framework on three types of data: hand video sequences, aerial images of cars as well as face images. We compare our method to a standard boosting framework, with access to the same ground truth, and show a reduction in the false alarm rate of up to an order of magnitude. Where possible, we compare our method to the state-of-the art, which requires pose annotations of the training data, and demonstrate comparable performance.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Theory & Algorithms
ID Code:9363
Deposited By:Francois Fleuret
Deposited On:16 March 2012