Optical Flow Based Online Moving Foreground Analysis
This addresses the need for structured foreground data in video analysis for applications like surveillance or robotics, but it appears incremental as it builds on existing optical flow and clustering methods.
The paper tackles the problem of unshaped foreground masks from moving object detection by constructing an optical flow-based framework to analyze and segment them, resulting in instance-level information such as the number, location, and size of moving objects, with experimental results showing it performs well for practical applications.
Obtained by moving object detection, the foreground mask result is unshaped and can not be directly used in most subsequent processes. In this paper, we focus on this problem and address it by constructing an optical flow based moving foreground analysis framework. During the processing procedure, the foreground masks are analyzed and segmented through two complementary clustering algorithms. As a result, we obtain the instance-level information like the number, location and size of moving objects. The experimental result show that our method adapts itself to the problem and performs well enough for practical applications.