Target Detection Based On Robust Estimation And Independent Component Analysis

Target detection based on computer vision is one of the most important research subjects in some fields such as pattern recognition, image processing and so on. Higher requirement has been put forward by the complexity of environment and the run time of algorithm. How to enhance the robustness and environmental adaptability within real-time performance has become the prime focus of algorithm research. Target detection technology can be divided into static target detection and moving target detection. The research contents of this paper contain parameters estimation of static models and detection of moving target area. The research purpose is to improve the robustness and practicality of algorithms.In the process of model parameters estimation, a novel robust estimation algorithm based on bottom up idea is proposed to improve the robustness of ellipse parameter estimation when the environmental information such as scale and quantity of models is unknown. The method uses the process of classification and clustering. Experimental results show that the method has good performance in accuracy and robustness with no prior information.Real-time moving target detection is widely applied in monitoring field. The background difference is fast and easy to implement, but due to the restrictions of threshold, the adaptability is not good in complicated environment. Independent component analysis (ICA) is a kind of signal separation methods by using statistical characteristics of signals. Because the impact of local disturbance is not obvious in treating processing, ICA has gained more and more applications in the field of image processing. In this paper a novel motion detection method based on ICA is proposed. The method has the following advantages: it is self-adaptive, it needs no threshold, it can adapt to complicated environment, and the processing speed meets real-time requirement. Experimental results show the validity and robustness of the algorithm.The practicality of algorithm should be verified in system. This paper describes a visual system that the author participated in designing. The system can transplant different algorithms to implement targets detection and tracking. The system uses the ICA-based detection algorithm and the color-based tracking algorithm. Operation effects show that the system can accomplish the detection and tracking tasks.