Issue No. 02 - February (2012 vol. 34)
K. Ali , EPFL IC CVLAB, Lausanne, Switzerland
F. Fleuret , Centre du Parc, Idiap Res. Inst., Martigny, Switzerland
D. Hasler , CSEM SA, Neuchdtel, Switzerland
P. Fua , EPFL IC CVLAB, Lausanne, Switzerland
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, and 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.
real-time systems, object detection, pose estimation, face images, real-time deformable detector, object detection, AdaBoost procedure, pose estimators, video sequences, aerial images, Feature extraction, Image processing, Object detection, Training data, Image edge detection, Machine learning, Learning systems, object detection., Image processing and computer vision, machine learning
D. Hasler, P. Fua, F. Fleuret and K. Ali, "A Real-Time Deformable Detector," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 225-239, 2012.