A Target Tracking Method Based On Curve-fitting

In recent years, with the development of the areas such as urban security, battlefield reconnaissance of surveying and mapping, image-guided navigation, anti-terrorism, counter-terrorism, and navigation guidance, human-computer interaction, speech recognition, public health and other areas, computer vision technology has captured great attentions of many engineering personnel, scientific and technical workers, university scholars, security administration and defense research institutes and other personnel or departments. Obtaining an accurate target object and continuous tracking an object in a video stream is the foundation of computer vision used in these areas mentioned as above, and it also become one of the hot field of computer vision.Overall, the video target tracking methods can be classified into different types, but the MeanShift algorithm and the CamsShift algorithm which based improvement with MeanShift algorithm and other algorithm which combine with Kalman filtering. While both of those algorithms are based on the target’s color characteristics, thus, these algorithms would not accurately tracking the targets in this situation which the background image’s color features and the foreground image’s color features almost same.Aiming at this problem, this paper proposed a target tracking method based on curve fitting, in this method, first, the target object should be marked artificial, then predicting few future target objects positions based on the historical tracking position points, experiments proved this algorithm can validity tracking targets in some scenes.According to the video placement, object tracking can also be divided into variety of different methods, the paper mainly for fixed cameras, the situation objectives of the campaign, he main work includes the following aspects.First, a brief introduction to the significance of research on video tracking technology, and a summation of some works has already done by our predecessors, including the classification of video tracking scene, video tracking technology research status, video tracking technology classification and the difficult of the video tracking technology summary.Secondly, aimed at the first step in object tracking targets--obtaining an object, the article briefly describes the basic outlook of several detection methods, and methods for background modeling. And summarized and compared against the current more widely used single Gaussian background modeling, Gaussian mixture background modeling methods, laid the foundation for the following algorithm theory.Thirdly, a detailed description of MeanShift algorithm and CamShift algorithm is showing in this paper, summing up their shortcomings by experimentation.Last, Aimed at target tracking based on template matching bad real-time and template drift features. A target tracking method which based on target’s centroid points curve-fitting was proposed. First, the algorithm get a foreground image and remove the noise points by image morphology operation, then, the searching area will limit in the minimum target closure rectangle area based on the cure-fitting. Searching target in this area, updating fitting points with it’s centroid point if it meet the threshold else updating fitting points with predictive point. Updating search area. Compared with classical tracing algorithms showed it was effective and accuracy in object tracking.