Online Kernel Slow Feature Analysis for
Temporal Video Segmentation and Tracking
Further Details Contact: A Vinay 9030333433, 08772261612
Email: takeoffstudentprojects@gmail.com | www.takeoffprojects.com
Abstract
Slow feature analysis (SFA) is a dimensionality reduction technique which has been linked to how
visual brain cells work. In recent years, the SFA was adopted for computer vision tasks. In this
paper, we propose an exact kernel SFA (KSFA) framework for positive definite and indefinite
kernels in Krein space. We then formulate an online KSFA which employs a reduced set
expansion. Finally, by utilizing a special kind of kernel family, we formulate exact online KSFA
for which no reduced set is required. We apply the proposed system to develop a SFA-based
change detection algorithm for stream data. This framework is employed for temporal video
segmentation and tracking. We test our setup on synthetic and real data streams. When combined
with an online learning tracking system, the proposed change detection approach improves upon
tracking setups that do not utilize change detection.
Existing Method:
In contrast to PCA however, SFA considers the temporal information to find the most descriptive
components that vary slowest over time
Demerits
High complexity
Proposed Method
We apply the proposed system to develop a SFA-based change detection algorithm for stream data.
This framework is employed for temporal video segmentation and tracking.
Merits:
Execution time is less
Further Details Contact: A Vinay 9030333433, 08772261612
Email: takeoffstudentprojects@gmail.com | www.takeoffprojects.com