智能车辆的行人检测技术研究（Research on pedestrian detection of intelligent vehicle）

1、智能车辆的行人检测技术研究（Research on pedestrian detection of intelligent vehicle）Research on pedestrian detection of intelligent vehicleAuthor: 2010-07-05 16:16:07 author / Yang Dapeng Gao Xiangdong Liu Hong School of mechanical engineering Zhengzhou University Laiwu Steel Group Ltd Song Hanjun source: text si

2、ze: small in large.To reduce the occurrence of traffic accidents and casualties have become the focus of the whole society, to carry out the research on pedestrian detection technology, timely inform the driver of the vehicle in front of the pedestrian, and safety warning, to reduce and avoid signif

3、icant vehicle pedestrian collision accident.Pedestrian detection statusThe pedestrian detection faces more complex work environment and high performance requirements, therefore become the focus of research in interdisciplinary computer science, image processing, automatic control, artificial intelli

4、gence and pattern recognition. The main methods of pedestrian detection are sensor based, feature extraction, human based model, statistical classification, template matching, etc.Sensor based approachAt present, the main sensors used in pedestrian detection are monocular vision, stereo vision, infr

5、ared imaging, microwave radar and laser range finder. These sensors carry out non-contact detection of the surrounding environment of vehicles to obtain information about pedestrians, vehicles, obstacles and so on.Vision is an important means of human observation and to understand the world, the nee

6、ds of the driver when driving the 90% information from the visual system, traffic signals, traffic patterns, such as road signs can be regarded as the environment and the drivers visual communication language. Monocular vision has the advantages of rich information content, large information area, m

7、ulti lane detection, simultaneous road detection and obstacle identification, and low maintenance cost. However, the image information obtained by it is very complex, and the amount of data to be processed is very large, and it is also affected by light, weather and occlusion. Based on the pedestria

8、n detection method under monocular moving, the pedestrian is decomposed into several components for identification.Stereo vision is to observe the same thing from two or more points of view, so as to obtain the perception image from different angles, and to obtain the 3D information of the scene by

9、calculating the parallax between the pixels of the image by triangulation. The method can obtain the distance between pedestrians and other vehicles, and partially solve the problem of pedestrian occlusion. Pedestrian segmentation is performed by stereo vision segmentation algorithm, and then neural

10、 network is used for target classification and recognition.Infrared imaging is based on the temperature of the object and the amount of heat emitted. It can work during the day and night, as well as in rain and snow. Because there is no color or texture and no light, pedestrian identification is eas

11、ier, but easily affected by heat sources. Based on infrared imaging real-time pedestrian detection system, a probabilistic template is designed, then template matching is used to determine whether pedestrians are present in the image.Radar technology and laser range finder can detect the depth infor

12、mation around the vehicle, and it is not affected by climate, light and so on. It has great advantages in speed measurement and distance measurement. Radar and laser range measurement have significant advantages in obtaining long distance image information, but their resolution is low, which is not

13、conducive to classification. Radar based urban traffic scene characteristics aim at exploring all the information that can be obtained from radar sensors and selecting appropriate features for pedestrian detection.A single sensor can not complete the needs of pedestrian detection, therefore, multi-s

14、ensor fusion becomes a new direction. Specifically, different types of sensors are used to detect different types of information, and then comprehensive processing of information, make full use of the advantages of various sensors, so as to achieve the best results. The method of detecting informati

15、on fusion between single millimeter wave radar and Dan Guangxue camera adopts radar for distance measurement and camera for lane mark detection.Method based on feature extractionThe common features of pedestrian detection are appearance features, motion features and abstract features. Abstract featu

16、res can be used to describe appearance features or to represent motion features.Appearance features are used to describe pedestrian shapes, sizes, textures and other static information in images. The method based on appearance feature avoids the change of background image caused by the movement of c

17、amera, and can effectively identify the stationary and walking pedestrians in the road. Researchers took the shape of the pedestrian from the image sequence and then matched the designed template to determine if pedestrians were present. Because of the diversity of shape information displayed by ped

18、estrians, the complexity of pedestrian background and partial occlusion, they increase the difficulty of pedestrian detection.Movement characteristic is used to describe the change and law of pedestrian movement. The stiffness and periodicity of the moving body are analyzed by calculating the residu

19、al optical flow in the moving region,According to the non rigid pedestrian motion, the average residual optical flow is distinguished from the rigid vehicle motion, and the pedestrian is distinguished. Optical flow computing can detect independent moving objects without any information about the sce

20、ne in advance, and can be applied to environments with stationary backgrounds and moving backgrounds. However, several frames are needed to increase the processing time, and it is not convenient for real-time detection of pedestrians.Because of the limitations of individual appearance features and m

21、otion features, multi feature fusion has become a new research field. Taking into account the candidate region and a large number of complex scenes will make the recognition rate, only features to train a good classifier is difficult, so some people put forward the integration of shape feature and m

22、otion feature, a feature set, it is used to train the classifier. In addition, the superiority of the fusion method is demonstrated by comparing the two cases using only contour features and contour features combined with motion features.Method based on human body modelA model-based pedestrian detec

23、tion method is defined by defining the pedestrian shape model and matching the model at each part of the image to find the target. This method has explicit model, fast recognition speed, and can deal with partial occlusion, but it is difficult to construct suitable model. Pedestrian models are mainl

24、y one-dimensional human body model, two-dimensional human model and 3D human model.One dimensional model is the simplest human model because it can be modeled directly from a straight line in each part of the body, also known as a linear model. The contour of the travelers can be automatically segme

25、nted from the camera sequence images by using the background subtraction method, and a statistical shape model of pedestrians is established. Then, the linear point distribution model is established, and the principal component analysis is used to reduce the dimension and find the 8 dimensional defo

26、rmation space.A two-dimensional model of the human body is the most widely used models, such as a coarse-to-fine method for the detection of pedestrians, the one part of the bodys natural assembly modeling, the parts including the head and shoulders, torso and legs, with the first body detector to f

27、ind the pedestrian, then use the combination algorithm based on Bayesian decision to further determine the pedestrian candidate the experimental results show that this method has good detection performance.Three dimensional human body model is also called the three-dimensional model, mainly uses the

28、 ball, the ellipse column, the generalized cone platform and so on to describe the human body structural detail. This model has obvious advantages in dealing with attitude problems, angle change and occlusion problems, but this model requires more computing parameters. A three-dimensional model comp

29、osed of cylinders is obtained by using Calman filtering to estimate the parameters of the image model.Method based on statistical classificationBased on statistical classification, a classifier is learned by machine learning from a series of training data, and then the classifier is used to recogniz

30、e the input window. At present, the commonly used statistical classification methods are neural networks, support vector machines and Adaboost.The classification mechanism cannot meet the needs of users based on a single, so the researchers proposed classification mechanism combination, i.e. multipl

31、e single classifier according to the structure of organizing them, the comprehensive judgment of the target detection, and obtain the advantages of different classifiers.Template matching based methodThe template matching is the process of looking for known template image from the target image, the

32、template image translation and similarity calculation of the translation position of the template image and target image overlap in the target image, if the parameters meet the requirements set in advance, can be considered in the target image area representative template image the image. The area o

33、f the image in the target image position is obtained at the same time. Template matching based methods are able to detect and identify stationary pedestrians and moving pedestrians. Researchers designed a pedestrian detection algorithm for the design of intelligent vehicle based on vision, first the

34、 wavelet transform of the video frames from the camera mounted on the vehicle, and then use the multi-scale template matching to determine whether the frame contains information and location of pedestrians, pedestrians.Existing problems and ProspectsPedestrian detection is a research hotspot of inte

35、lligent vehicle, auxiliary driving and intelligent transportation, with the development of computer science, electronic technology, image processing, artificial intelligence and pattern recognition, pedestrian detection has made gratifying achievements in multi disciplines. Although many scholars ha

36、ve made useful attempts and achieved good results, pedestrian detection has been a challenging problem because of the large number of difficult problems to be solved. The main problems are as follows:(1) pedestrian detection objects: pedestrians wear a variety of colors and styles of clothing, may w

37、alk with a large area of occlusion;Pedestrians usually walk in out of order in the natural environment, there are a lot of people like the outline of objects and non standing pedestrians; the same attitude of people facing the camera angle is different, it will get the image on the pedestrian showed

38、 different shape; pedestrian detection is facing the open environment, to consider the different weather (rain snow, fog, etc.), light changes etc.(2) pedestrian detection method: with the deepening of the study, the existing methods have been improved, and new methods have appeared in large numbers. However, the existing methods are difficult to meet the requirements of fast detection speed, high detection rate and low false positive rate.(3) the performance evaluation system of comprehensive performance evaluation system for pedestrian detection: a standard metho