基于压缩感知理论的实时目标跟踪算法研究及系统实现

Real-time Object Tracking Research Base on Compressive Sensing and System Implementation

目标跟踪是计算机视觉的一个具有挑战性的方向,其基本思想是序列图像中根据目标在视频信息的空间和时间上相关性,从而确定感兴趣的目标在每一帧的位置和姿态以及目标的运动轨迹。目标跟踪的困难主要由于目标的形状及尺度的变化、目标不可预知性的运动以及目标所处的背景存在干扰观测的物体等引起。本课题使用一种基于压缩感知理论的高效的跟踪算法,确定跟踪的目标,在目标周围采集正样本,远离目标采集负样本,利用符合RIP原则的随机测量矩阵对提取的样本特征进行降维,并采用朴素贝叶斯分类器分类,同时更新分类器。在研究算法的基础上,利用Open CV图像视觉库及Qt开发框架进行系统的实现,完成具有多功能辅助的目标跟踪系统,其功能包括目标跟踪、人脸检测、开启摄像、视频播放等。

Object tracking is a challenging research direction of computer vision. The basic idea of object tracking is to ensure object position and attitude and the object trajectory based on the spatial-temporal correlation in video. Object tracking is one of the most difficult tasks in computer vision, and the difficulty is mainly due to shape change, the unpredictability of the motion and complicated background from the object. This topic employs a simple and efficient tracking algorithm based on compressive sensing. Determining the goal of tracking, first to collect the positive samples around the object, and collect the negative samples far away the object, after the dimension reduction by random sparse measurement matrix which satisfy the condition of RIP, we use naive Bayesian classifier to recognize the feature vectors. On the basis of the algorithm, we use Open CV image processing library and Qt completed the multi-function aid object tracking system consisting of object tracking, face detection, open capture, video display, etc.