title: Transferring boosted detectors towards viewpoint and scene adaptiveness.
abstract: In object detection, disparities in distributions between the training samples and the test ones are often inevitable, resulting in degraded performance for application scenarios. In this paper, we focus on the disparities caused by viewpoint and scene changes and propose an efficient solution to these particular cases by adapting generic detectors, assuming boosting style. A pretrained boosting-style detector encodes a priori knowledge in the form of selected features and weak classifier weighting. Towards adaptiveness, the selected features are shifted to the most discriminative locations and scales to compensate for the possible appearance variations. Moreover, the weighting coefficients are further adapted with covariate boost, which maximally utilizes the related training data to enrich the limited new examples. Extensive experiments validate the proposed adaptation mechanism towards viewpoint and scene adaptiveness and show encouraging improvement on detection accuracy over state-of-the-art methods.
authors.affiliation: Graduate University and the Institute ofComputing Technology, Chinese Academy of Sciences, Beijing 100190, China. jbpang@jl.ac.cn
authors.forename: Junbiao
authors.initials: J
authors.lastname: Pang
authors.forename: Qingming
authors.initials: Q
authors.lastname: Huang
authors.forename: Shuicheng
authors.initials: S
authors.lastname: Yan
authors.forename: Shuqiang
authors.initials: S
authors.lastname: Jiang
authors.forename: Lei
authors.initials: L
authors.lastname: Qin
mesh_terms: D000465:Algorithms; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated