Research on the Detection and Tracking of Moving Target based on Kernel Method

다국어 초록 (Multilingual Abstract)

The research work in this paper is in the field, the moving target detection spatiotemporal correlation and difference contour tracking algorithm based on a fixed background. The algorithm in the background under the condition of fixed to pay a smaller time complexity, the target detection and tracking has a good effect, so it has higher application value. This paper mainly focuses on the study of motion estimation and compensation algorithm to eliminate the temporal redundancy. Algorithm of detection and tracking of video moving object is a core subject in computer vision field, but also the key technology of intelligent video surveillance system. It combines the research achievements of artificial intelligence and other fields of pattern recognition, image processing, has been widely used in every field of security monitoring, intelligent weapons, video conference, video retrieval. Therefore, detection and tracking algorithm research has the extremely important theory significance and practical value. The starting point of this article is the subjective quality of image reconstruction of how to improve the accuracy of motion estimation and compensation after, to reduce the computation complexity of motion estimation algorithms, to improve the efficiency of motion estimation. This paper makes some studies on the redundant wavelet domain block matching motion estimation and compensation, then the video image for non-translational motion, the DT triangular mesh motion estimation and compensation in the redundant wavelet domain to do related research.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Related Work and Theory Analysis
• 3. Image Features Extraction based on Video Motion Nuclear Method
• A. Block Matching Method
• B. The correlation of the different block detection
• C. Fast adaptive motion estimation and compensation algorithm
• 4. Experimental and Results
• A. Describe the main algorithm experimental model
• B. SIFT algorithm for feature extraction
• C. Simulation results and analysis
• Conclusions
• References