A moving detection algorithm based on space-time background difference

Authors:
Mei Xiao;Lei Zhang;Chong-Zhao Han
Affiliations:
School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, China;Chang'an University, Xi'an, China;School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, China
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

Citing 8
Cited 0

Stationary background generation: an alternative to the difference of two images

Pattern Recognition
Pfinder: Real-Time Tracking of the Human Body

IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Sequence Analysis via Partial Differential Equations

Journal of Mathematical Imaging and Vision
Non-parametric Model for Background Subtraction

ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Detected motion classification with a double-background and a neighborhood-based difference

Pattern Recognition Letters
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information

MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Recursive Unsupervised Learning of Finite Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting moving objects, ghosts, and shadows in video streams

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.01

Visualization

Abstract

Based on the assumption that background figures have been extracted form the input image, we propose a method that can effectively detection the moving objects from image sequence in this paper. The background difference, background difference based neighborhood pixels and frame difference information are fused to get the seed points of real moving object, only the blobs in moving detection based on background difference that intersect with seed pixels are selected as the final moving segmentation result, then we can obtain the true moving foreground. Simulation results show that the algorithm can avoid the false detection due to the wrong in background model or background update and can handle situation where the background of the scene contains small motions, and motion detection and segmentation can be performed correctly.