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 [BibTeX] [Marc21]
A real-time deformable detector.
Type of publication: Journal paper
Citation: Ali_TPAMI_2011
Publication status: Accepted
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 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.
Projects Idiap
Authors Ali, Karim
Fleuret, Francois
Hasler, David
Fua, Pascal
Added by: [UNK]
Total mark: 0
  • Ali_TPAMI_2011.pdf