title: Domain adaptation for pedestrian detection based on prediction consistency.
abstract: Pedestrian detection is an active area of research in computer vision. It remains a quite challenging problem in many applications where many factors cause a mismatch between source dataset used to train the pedestrian detector and samples in the target scene. In this paper, we propose a novel domain adaptation model for merging plentiful source domain samples with scared target domain samples to create a scene-specific pedestrian detector that performs as well as rich target domain simples are present. Our approach combines the boosting-based learning algorithm with an entropy-based transferability, which is derived from the prediction consistency with the source classifications, to selectively choose the samples showing positive transferability in source domains to the target domain. Experimental results show that our approach can improve the detection rate, especially with the insufficient labeled data in target scene.
authors.affiliation: Key Laboratory of Intelligent Information Processing, Universities of Shandong (Shandong Institute of Business and Technology), Yantai 264005, China ; School of Computer Science and Technology, Shandong Institute of Business and Technology, Yantai 264005, China.
authors.forename: Yu
authors.identifier: 0000-0002-4727-5218
authors.initials: Y
authors.lastname: Li-ping
authors.affiliation: School of Computer Science and Technology, Shandong Institute of Business and Technology, Yantai 264005, China.
authors.forename: Tang
authors.initials: T
authors.lastname: Huan-ling
authors.affiliation: Key Laboratory of Intelligent Information Processing, Universities of Shandong (Shandong Institute of Business and Technology), Yantai 264005, China.
authors.forename: An
authors.initials: A
authors.lastname: Zhi-yong
mesh_terms: D019277:Entropy; D008962:Models, Theoretical; D011336:Probability; D016138:Walking / Q000706:statistics & numerical data*