Detection Of Multi-target Image

With rapid development of pattern recognition and artificial intelligence, both domestic and international researchers pay a lot of attention into the technology of multi-target image detection. Image detection and recognition has powerful prospects and huge potential economic value in area of science and security. At the same time, multi-target image detection technology plays an important role both in people's life and business activities.As the proceeding of society and technology, people propose higher demand on image detection and recognition technology. The real-time performance and detection rate are two primary measure standards, and further directions of this technology. However, some problems, such as diversity and block of the target, complex background, external environment and so on, become the bottleneck of the Image detection technology, all of them pull down the speed and accuracy of the final result. The main purpose of this paper is to improve the overall performance of the image detection system, and realize multi-target detection. Brief work and results are shown as follows:1. Using DirectShow to architecture video capture system, as a modle for capturing target image, Combining with WDM video capture to establish the complete video preview model. The DirectShow could make complex data stream transport easy and effective on multi-hardware platforms, it can ensure the real-time performance of the following detecting operations.2. To improve training speed and the precision of detection result, resolving the problem of large computation and spending long time in traditional AdaBoost method, we use a definite threshold range in feature selection, when training the weak classifier, so as to reduce search time, to improve threshold optimization, to reduce the number of weak classifier, and finally to improve the training speed.3. To satisfy the demands of real-time dynamic image detection, this passage combined stratification method with half pixel match algorithm to match the image accuracy and real-time performance, and largely reducing the match time while performing detection. Meanwhile, we study the theory of wavelet transform. According to the feature of frequency domain characterization in wavelet, we use weighted average method into the low-frequency region, and the method based on region into the high-frequency region.4. Using Adaboost algorithm to train and detect human face in multi-postures, and then obtain the position eyes based on the region of face. Here we used Gabor wavelet transform, DCT transform and adaboost training classifier to locate human eyes. And conduct a simple comparative analysis of their strengths, weaknesses and algorithm performance. Then we perform binarization and integral projection transformation, according to geometry knowledge of eyes, to locate the eyes and mark them. Because of the face detection, the image has normalized effectively, narrowing the range of search area into a smaller range. Processing the image again, the impact of light is relieved effectively. We integrate a variety of methods, and the system can locate eyes quickly with a improved performance of the detection.Comparing to the traditional method, the Experiment results shows that the performance of this system significantly improved. While there are also some problems, for example, detection result will fall down when the target has a big rotation. The problem of matching rotation target needs further research.