Experiments in the machine interpretation of visual motion
Experiments in the machine interpretation of visual motion
Markov random field modeling in computer vision
Markov random field modeling in computer vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatiotemporal Segmentation Based on Region Merging
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation using evolutionary computation
IEEE Transactions on Evolutionary Computation
Semiautomatic segmentation and tracking of semantic video objects
IEEE Transactions on Circuits and Systems for Video Technology
Region-based representations of image and video: segmentation tools for multimedia services
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
A new video segmentation method of moving objects based on blob-level knowledge
Pattern Recognition Letters
Segmentation of Moving Objects with Information Feedback Between Description Levels
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Context Data to Improve Association in Visual Tracking Systems
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Video Tracking Association Problem Using Estimation of Distribution Algorithms in Complex Scenes
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
An efficient design of a nearest neighbor classifier for various-scale problems
Pattern Recognition Letters
Reconstruction of degraded images using genetic algoritm for archive film restoration
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Entropy based region selection for moving object detection
Pattern Recognition Letters
Research on genetic segmentation and recognition algorithms
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Moving object detection using Markov Random Field and Distributed Differential Evolution
Applied Soft Computing
| Hi-index | 0.00 |
The current paper proposes a genetic algorithm (GA)-based segmentation method that can automatically extract and track moving objects. The proposed method mainly consists of spatial and temporal segmentation; the spatial segmentation divides each frame into regions with accurate boundaries, and the temporal segmentation divides each frame into background and foreground areas. The spatial segmentation is performed using individuals that evolve distributed genetic algorithms (DGAs). However, unlike standard DGAs, the individuals are initiated from the segmentation result of the previous frame, then only unstable individuals corresponding to actual moving object parts are evolved by mating operators. For the temporal segmentation, adaptive thresholding is performed based on the intensity difference between two consecutive frames. The spatial and temporal segmentation results are then combined for object extraction, and tracking is performed using the natural correspondence established by the proposed spatial segmentation method. The main advantages of the proposed method are twofold: first, proposed video segmentation method does not require any a priori information; second, the proposed GA-based segmentation method enhances the search efficiency and incorporates a tracking algorithm within its own architecture. These advantages were confirmed by experiments where the proposed method was successfully applied to well-known and natural video sequences.