Issue No. 03 - March (2011 vol. 33)
Quan Yuan , Sony Electronics Inc., San Jose
Ashwin Thangali , Boston University, Boston
Vitaly Ablavsky , Boston University, Boston
Stan Sclaroff , Boston University, Boston
Object detection is challenging when the object class exhibits large within-class variations. In this work, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly learned in a multiplicative form of two kernel functions. Model training is accomplished via standard SVM learning. When the foreground object masks are provided in training, the detectors can also produce object segmentations. A tracking-by-detection framework to recover foreground state in video sequences is also proposed with our model. The advantages of our method are demonstrated on tasks of object detection, view angle estimation, and tracking. Our approach compares favorably to existing methods on hand and vehicle detection tasks. Quantitative tracking results are given on sequences of moving vehicles and human faces.
Object recognition, object detection, object tracking, pose estimation, kernel methods.
Q. Yuan, A. Thangali, V. Ablavsky and S. Sclaroff, "Learning a Family of Detectors via Multiplicative Kernels," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 514-530, 2010.