Online Kernel Slow Feature Analysis for Temporal Video Segmentation and Tracking - 2015 PROJECT TITLE : Online Kernel Slow Feature Analysis for Temporal Video Segmentation and Tracking - 2015 ABSTRACT: Slow feature analysis (SFA) is a dimensionality reduction technique that has been linked to how visual brain cells work. In recent years, the SFA was adopted for pc vision tasks. In this paper, we tend to propose an actual kernel SFA (KSFA) framework for positive definite and indefinite kernels in Krein house. We have a tendency to then formulate an online KSFA that employs a reduced set expansion. Finally, by utilizing a special quite kernel family, we formulate actual online KSFA for that no reduced set is needed. We have a tendency to apply the proposed system to develop a SFA-based modification detection algorithm for stream data. This framework is employed for temporal video segmentation and tracking. We tend to test our setup on artificial and real information streams. When combined with an online learning tracking system, the proposed modification detection approach improves upon tracking setups that do not utilize amendment detection. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Feature Extraction Video Signal Processing Object Detection Set Theory Computer Vision Object Tracking Unsupervised Learning Slow Feature Analysis Online Kernel Learning Change Detection Temporal Segmentation Tracking Attentive Monitoring of Multiple Video Streams Driven by a Bayesian Foraging Strateg - 2015 Random Geometric Prior Forest for Multiclass Object Segmentation - 2015
