Video tracking plays a very important role in computer vision and has extensive applications in the fields of video surveillance, man-machine interface, automobile navigation and intelligent transport system. However, the practical applications of the video tracking technology still have many technical difficulties including complicated background, non-rigid object, illumination change or masked object, and these technical issues make the visual object tracking very difficult, and both stability and accuracy of the tracked object will be affected by the aforementioned technical issues. Nonetheless, video tracking primarily analyzes and processes information of the characteristics such as the color, shape and lines of an object continuously to estimate the center position and size of a target to achieve the tracking effect.
At present, there are many different tracking algorithms available in the market, and the features of these algorithms are different, wherein Mean Shift algorithm is a highly efficient visual object tracking method, and CamShift algorithm switches the Mean Shift algorithm to be adaptive and automatically adjusts the size of an object window in order to fit the size of the object that changes with time, and the CamShift algorithm is a highly efficient and stable target tracking method and thus capturing extensive attention.
However, CamShift is an improved algorithm of Mean shift. Although CamShift is a high-speed algorithm, it mainly uses a color characteristic as the basis of tracking. Since the computation method is to convert the similarity of colors into probability and then calculate the mass center from the probability. If a target has a background with similar colors or a larger object with similar colors, the tracking of the original object will be interfered to cause a failure of tracking. In other words, this algorithm simply using the similarity of colors to calculate probability is often interfered by other larger objects or the background having similar colors of a target, and misjudgments or errors of the object tracking occur frequently.
In view of the problems above, the inventor of the present invention based on years of experience in the related industry to conduct extensive researches and experiments, and finally invented a visual object tracking method in accordance with the present invention to overcome the foregoing problems.