基于dsp的目标跟踪算法的分析与实现word格式论文.docxVIP

Visual tracking is always being an active research topic in Information technology, because of its wide application in the automatic object recognition, intelligent surveillance, vehicle navigation, vehicle navigation, human computer interaction, and other disciplines. Photoelectric imaging visual tracking for over-ground surveillance and tracking, used into target automatic tracking and ranging in the vehicular photoelectric platform.This thesis focuses on visual tracking algorithm based on DSP. To overcome the presences of noise，occlusion, background clutter and illumination changes in visual tracking algorithm, it is need to develop a robust visual tracking framework. The particle filter framework built in thiswork casts the tracking problem as finding sparse representation of candidate target. And then, proposing a solution to settle up the sample impoverishment problem brought from the resampling method in the particle filter framework. Here are researching contents in this work:Bring up the genetic bounded resampling algorithm. Under the condition of no sacrificing the resampling accuracy, use the bounded samples resampling to reduce the time-consuming calculation of the sampling values. Then, use genetic algorithms to increase the diversity of the sample in order to reduce sample degradation problem, brought from resampling in the particle filter framework.Detect occlusion. Using dynamic template to capture surface changes, to stop the unsuited tracking results from adding into the template set, it detected the occlusion before the results added into the set. And then control the updating of templates.Display the tracking results visually. Build the DSP platform, using the CCSLink tools to combine the DSP and MATLAB in order to perform the data transmission. The DSP chip call tracking algorithm and return the results into MATLAB workspace. Then the tracking results are turned into intuitive picture format through the capability of powerful data processing in