Research On People Tracking Theory And Algorithm

The real-time tracking for moving people is to process and analyze the sampled image and to obtain the information about the body. The system has the wide use and economical worth in the smart surveillance and monitor, high-grade Human-Computer interface, motion-based diagnosing, etc. So, this field has been become one of the most active research area in computer vision recently. How to detect the moving object from the complex background becomes the significant discussing topic especially and extensive attention has paid to it. As we know, image de-noising is the important part of the image processing. So, the tracking problem is discussed under different background environment in this paper, it mainly contains image de-noising in wavelet domain, image edge enhancing, target detecting in wavelet domain, moving object detecting on complex background with Baysian rules, moving object analyzing and object tracking with Kalman filer, etc.The real-time tracking system for people is a general frame of real-time monitor, the concrete problems are analyzed and several new algorithms are proposed here. The main ideas are as follows:1. The image which contains target must be pre-processed in order to decrease the interference of the noises. So, the different algorithms are proposed in the paper:(1) Image denoising by the optimal soft threshold: the shrinkage of the wavelet coefficients are discussed further on the basis of Donoho theory and an optimal soft threshold is obtained to finish the image denoising. The MES rule is used to optimize the original soft threshold and the optimal denoising algorithm is deduced for different noises. The theory and experiment has proved that a better denoising result can be obtained than original method.(2) Image de-noising based on double Haar transform MAP estimate: At first, the wavelet basis selection is discussed. Double Haar transform not only has the better edge detecting property but also can smoothen the noises more effective. The new method gives a wavelet shrinkage algorithm based on MAP estimate and discusses its application in image de-noising. The correlation between the double Haar transform coefficients are used to obtain the optimal estimate. This shrinkage algorithm can get better de-noising result than the original soft threshold.(3) Self-adaptive edge enhancing algorithm: Edge enhancing is an important domain in image processing, and it is used to sharpen the blurred image. But, the present method is sensitive to noise. In order to raise the details protection abilities of the median filter we can use the multi-median filter. Because the output between the multi-median filter and mean filter at the image edge part is larger than the image flat part, the difference is used as the threshold to propose a self-adaptive edge enhancing algorithm. This method has the ability to constrain noises when the image edge is enhanced and it has the better application prospects.2. Detecting object correctly is a key step when the moving people is tracked, so, the new detecting algorithms are discussed under different circumstances in this paper and several improved algorithms are proposed:(1) Moving object detecting algorithm based on Baysian rules: In the object detecting system, the threshold selection is a key problem and it determines the accuracy of the detecting result. If the experience value is selected as the threshold to separate the object from the background, we can not obtain the ideal result sometimes. The new algorithm considers the limitation to define the object region by the experience value, and combines with the Baysian rulers, analyzes the horizontal of the binary image with a new dynamic threshold, ensures the object region complete and without noises. The method can improve the influence of changing light and make the detecting result more accurate.(2) Moving object detecting algorithm based on moving window double Haar transform: This mean does not analyze object in temporal domain any more, it starts from wavelet domain, and proposes a new algorithm. Before detecting object, the edge of de-noising difference image is enhanced and make the object region more obvious, then, the feature image is obtained with the energy character to differentiate background and object, and the judging threshold is obtain based on the histogram of the feature image to finish the image binarization. The experimental result shows that the new algorithm can get better detecting result.(3) The dynamic background detecting algorithm based on Baysian model: We usually meet complex background when we track the object. Baysian model is combined with kernel density function to give a new detecting object method in probability space. At first, the kernel density function of background and foreground is built based on the image correlation. Then, the prior probability of the background and foreground is estimated with Baysian theory. At last, a dynamic threshold is obtained to classify the target and background.3. We should track the moving object after we detect he (or she or them). The following algorithm is proposed in this paper for people tracking:Fast Kalman filter algorithm for moving object tracking: In order to track the moving object correctly, we should predict the position of the body in the next time according to the present frame and revised the position. Kalman filter can solve the problem and it can get the ideal tracking result. The new algorithm defines the state function at first based on the Kalman theory, then, deduces the value of the augment matrix and constant matrix. Experimental result shows that the method can not only track the object correctly but also improve computing speed.The experimental results of the indoor and outdoor circumstances show that the above-mentioned means can be used to detect and track moving people correctly.