In order to resolve false segmentation and false tracking problems caused by the influence of complex harbor background during IR moving target detection, a harbor background suppression approach is presented. Firstly, Sky-sea line region can be obtained by Otsu segmentation, which is applied to split images obtained through wavelet transform. Secondly, harbor background suppression point in sequential images can be located by multilevel filter. Finally, harbor background suppression can be realized according to those background suppression points. The proposed approach is validated by using actual IR in complex harbor background to realize background suppression. Experiment results indicate the feasibility and effectiveness of the proposed method.
An improved algorithm integrating wavelet decomposition, multilevel filtering, and an additive operator splitting (AOS)-based level-set framework for infrared small target detection is proposed. This model has two components: a filtering operation, and level-set evolution. In the filtering step, the original image is first decomposed using a wavelet transform. After determining the location of sea-sky line, we construct a subimage based on the sea-sky-line position, and then execute multilevel filtering on this subimage. This filtering framework provides the input image for the level-set evolution. Using the level-set formulation, complex curves can be detected while naturally handling topological changes of the evolving curves. To reduce the computational cost required by an explicit implementation of the level-set formulation, a new solver named AOS is proposed. Additionally, the quantitative analyses for our algorithm are also given. Experiments on real infrared image sequences indicate that our method is efficient and robust.
We propose a moving objects segmentation method for color image sequences based on the piecewise constant Mumford-Shah model (also known as the C-V model) solving by the semi-implicit additive operator splitting (AOS) scheme, which is unconditionally stable, fast, and easy to implement. The method first uses the Gaussian mixture model for background modeling and then subtracts the background to obtain the moving regions that are the handling objects of our method. As a result of the introduction of the AOS scheme, we could use a rather large time step and still maintain the stability of the evolution process. Additionally, the method can easily be parallelized because the AOS scheme decomposes the equations into a sequence of one-dimensional (1-D) systems. The experimental results demonstrate that under real moving objects video tests, the AOS scheme accelerates the evolution of the curve and significantly reduces the number of iterations, and also demonstrates the validity of our method.
An efficient algorithm based on PMHT and FCM is presented to track and detect the infrared multiple targets from the
sequences of IR. Candidate targets can be obtained and saved by preprocessing IR data, and the true target can be
detected from the above candidate targets through tracking those targets by PMHT algorithm and prior knowledge. Data
association and target detection can be realized by FCM. At the same time, target' disappearing and new targets'
emerging phenomena occasionally arise during target tracking. This problem can be solved by 'memorizing and filling'.
The proposed approach is validated using actual infrared image sequences. Experiment results indicate high performance
of the proposed method.
An efficient approach based on wavelet transform is presented to detect the variation
degree of dynamic background. Firstly, some candidate regions can be obtained by processing IR
data through wavelet transform, then the degree of the difference between current and referenced
background can be judged through detecting the difference of the above feature regions. Finally,
the small moving target can be detected by improved eliminating background. At the same time,
data association and robust tracking of the target can be realized by fuzzy inference. In addition,
target' disappearing and new targets emerging phenomena occasionally arise during target tracking.
This problem can be solved by 'memorizing and filling'. The proposed approach is validated
using actual infrared image sequences. Experiment results indicate the feasibility and effectiveness
of the proposed method.