Optical Flow Based Online Moving Foreground Analysis

11/18/2018
by   Junjie Huang, et al.
0

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.

READ FULL TEXT

page 1

page 3

page 4

research
07/13/2018

Optical Flow Based Real-time Moving Object Detection in Unconstrained Scenes

Real-time moving object detection in unconstrained scenes is a difficult...
research
01/29/2018

Improving Multiple Object Tracking with Optical Flow and Edge Preprocessing

In this paper, we present a new method for detecting road users in an ur...
research
04/25/2019

On guiding video object segmentation

This paper presents a novel approach for segmenting moving objects in un...
research
02/28/2018

A Feature Clustering Approach Based on Histogram of Oriented Optical Flow and Superpixels

Visual feature clustering is one of the cost-effective approaches to seg...
research
11/18/2018

An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes

Real-time motion detection in non-stationary scenes is a difficult task ...
research
06/24/2021

Class agnostic moving target detection by color and location prediction of moving area

Moving target detection plays an important role in computer vision. Howe...
research
07/01/2020

FlowControl: Optical Flow Based Visual Servoing

One-shot imitation is the vision of robot programming from a single demo...

Please sign up or login with your details

Forgot password? Click here to reset