AAAI Publications, Twenty-Ninth AAAI Conference on Artificial Intelligence

Font Size: 
Metric Learning Driven Multi-Task Structured Output Optimization for Robust Keypoint Tracking
Liming Zhao, Xi Li, Jun Xiao, Fei Wu, Yueting Zhuang

Last modified: 2015-03-04

Abstract


As an important and challenging problem in computer vision and graphics, keypoint-based object tracking is typically formulated in a spatio-temporal statistical learning framework. However, most existing keypoint trackers are incapable of effectively modeling and balancing the following three aspects in a simultaneous manner: temporal model coherence across frames, spatial model consistency within frames, and discriminative feature construction. To address this issue, we propose a robust keypoint tracker based on spatio-temporal multi-task structured output optimization driven by discriminative metric learning. Consequently, temporal model coherence is characterized by multi-task structured keypoint model learning over several adjacent frames, while spatial model consistency is modeled by solving a geometric verification based structured learning problem. Discriminative feature construction is enabled by metric learning to ensure the intra-class compactness and inter-class separability. Finally, the above three modules are simultaneously optimized in a joint learning scheme. Experimental results have demonstrated the effectiveness of our tracker.

Keywords


computer vision; object tracking; metric learning; multi-task learning; structured output learning

Full Text: PDF