|其他题名: ||Technologies of Recognizing and Tracking Motion Object under Complex Background
|英文摘要: ||Object recognition and tracking technique based on images has been widely used in many fields of defense and civil economic constructions. It combines advanced technologies and research achievements in computer vision, image processing, pattern recognition, compute application and other relative fields. Extracting features that show object's intrinsic characteristics and have strong adaptabilities is the kernel of recognition research all along. The traditional invariant features in translation, rotation and scaling transformation can't satisfy the needs of advanced object recognition. So the research about 3D invariance prospered in recent years is becoming one of the active research fields in computer vision. In practical application, the feature space is generally non-linear divisible. So the method of recognition need have strong classification ability. It affects the performance of stable tracking in complex environment that because of the unpredictable circumstance (light、weather、visibility) and noise interference , especially from long distance. So it become the emphases in this research that how to realize recognizing motion objects in 3D space and stable tracing under the complex background.
Based on the brief introduction of the development of motion object recognition and tracing, and its relative fundamental theories and method, the discussion and research is processed from several aspects: feature invariant of object, the method of object recognition and stable tracing and so on.
In the beginning of the research of extracting feature invariant of motion object, several invariant in 2D space were analyzed. Then the research was emphasized on 3D invariant. Through the analysis of the imaging model and the relation between affine and perspective transformation, Affine-Invariant Discrete Cosine Transformation Descriptor was proposed. Firstly, the boundary of an object was parameterized by differential area, and then the parameterized boundary was transformed by discrete cosine transformation. Finally, based on this method, a set of descriptors was obtained, which have affine invariant. Especially, they are invariant as the change of viewport in recognition of 3D motion object. Through analysis of theory, the computational process of this method was given. Many planes of different poses in 3D space were simulated. The experiment results show that values of each kind of character are stable correspondingly.
The design of classifier is also one key problem of object recognition. Using the method based on kernel Fisher discriminant can realize non-linear classification. The method used the same thinking of kernel function as in Support Vector Machine. After mapping the data non-linearly into a certain higher dimensional feature space, Fisher’s linear discriminant was used in this feature space. A method of 3D motion object recognition was proposed. Affine-Invariant Discrete Cosine Transformation Descriptor was taken as feature, combining kernel Fisher discriminant. The experiment results show that the method improves the discrimination effectively.
Except using feature classifier to recognize object, the method based on correlation is a better way to realize object recognition. A method of regarding contour with affine invariant as feature was proposed to recognizing. Firstly, contour curve of object was parameterized by an affine variable. Then parameterized curve of object waiting for recognition was projected orthogonally. So it could keep better correlation with the object of the same class in store. Finally, normalized related function was regarded as the criterion of recognition. The experiment results show that the correlation of objects of the same class is more than 0.9 in the 3D’s change of pose.
In the motion object tracing, centroid tracking is better to the object of smaller size. In order to realize centroid tracking in real-time images，a method of segmentation is proposed. It combined the statistic of the gray scale in track and background gate of two contiguous frames with a smoothing value. Furthermore, according to the classification criterion built by Bayesian decision theory, the background and the object were classified. The experiment results show that this proposed method can avoid the effect that attributes to the wave of gray scale in some frames of an image sequence, and can segment the object from an image better.
Multi-mode tracking techniques fuse multiple tracking algorithms in order to promise exact tracking. So, when centroid tracking was not stable, the method based on feature points matching was used. Edge was detected by SUSAN principle. A new method of corner detect basing multi feature was proposed. It has higher precision and strong noise rejection. The different feature points were used in different conditions, and combining the improved Hausdorff distance to matching. A project of tracing object was improved. It promises stability of tracking in the complex background even in the condition of appearing the problem of occlusion.
In conclusion, some innovated methods are developed to solve the difficulties in the object recognition and tracking and engineering practicality. The work of this dissertation has important reference to the future development of optics-electronics detection and image processing.|
|Appears in Collections:||光电技术研究所博硕士论文_学位论文|
|File Name/ File Size
杨昕梅. 复杂背景下运动目标识别和稳定跟踪技术研究[D]. 光电技术研究所. 中国科学院光电技术研究所. 2007.