Realization Of Infrared Image Segmentation And Target Tracking Algorithm Based On Multi-Core DSP

Image segmentation is one of the most important parts in the field of digital image processing and computer vision.It is one of the most important problems in image analysis and target tracking.It is also one of the classic problems in image processing.The quality of the image segmentation directly affects the subsequent image processing.Image tracking technology is based on this.Firstly,the background and present situation of image segmentation and object tracking are expounded.Then,some classic methods of image segmentation are explained.The key techniques are introduced in detail.Then several threshold segmentation algorithms are validated and analyzed by experiments.Aiming at the shortcomings of classical methods,this paper designs and implements a segmentation method for ground infrared images in complex background.Experiments show that the method has good segmentation performance.Based on the image segmentation,this paper focuses on the tracking method based on intra-frame information,that is,using the inter-frame correlation and intra-frame information,the Kalman prediction tracker and the Mean-Shift algorithm are designed.Matching Tracker experiments show that the two trackers can achieve the specified target tracking,but comparing two trackers,Mean-Shift has better stability performance.In the end,based on the research of target tracking algorithm,the algorithm is implemented on DSP and FPGA hardware system.The results show that the system can satisfy the requirement of reliability and real-time.The performance of the tracking result is tested by the software platform.