title: Learning a family of detectors via multiplicative kernels.
abstract: 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.
authors.affiliation: US Research Center, Sony Electronics, Inc., San Jose, CA 95112, USA. quan.yuan@am.sony.com
authors.forename: Quan
authors.initials: Q
authors.lastname: Yuan
authors.forename: Ashwin
authors.initials: A
authors.lastname: Thangali
authors.forename: Vitaly
authors.initials: V
authors.lastname: Ablavsky
authors.forename: Stan
authors.initials: S
authors.lastname: Sclaroff
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation*; D021621:Imaging, Three-Dimensional / Q000295:instrumentation; D007858:Learning; D008390:Markov Chains; D009038:Motion; D018986:Motor Vehicles; D009716:Numerical Analysis, Computer-Assisted / Q000295:instrumentation*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique / Q000295:instrumentation