Study On Pedestrian Head Detection Method For Public Environment

In recent years,with the growing concern about public safety issues,the automatic detection of pedestrians in the public environment has become a hot topic in the field of intelligent surveillance and computer vision.Usually,the pedestrian may be in various dynamic or static states,and the gestures of body are always changing.Moreover,when the pedestrian is moving,mutual occlusion may arise especially in the complex scene.These challenges largely increase the difficulties of pedestrian detection.Compared to other parts of the human body,the human head is not easy to be occluded,and the boundary of the contour is usually distinct.Therefore,it is a reasonable idea to find position of pedestrian by detecting the pedestrian head.In this paper,we study the problem of human head detection in complex scenes,and focus on developing an effective human head detection algorithm in the public environment.The main contents of this paper are as follows:(1)The problem of pedestrian head detection is analyzed under the condition of fixed camera.Firstly,the moving foreground regions are obtained based on the background subtraction technique.Then,the regions of interest are located by analyzing the gradient and direction amplitude of the human head contour.At the same time,the K-mean clustering method is adapted to eliminate pseudo candidate regions.Finally,the SVM classifier is trained to classify the correct head position.The experiment shows that this method can keep up with high accuracy while speeds up the running time of the algorithm.(2)A head detection algorithm based on moving camera is proposed.When the camera is placed on a moving object such as a mobile terminal or a vehicle,the background detection method becomes unreliable.To solve this problem,the method of image segmentation is adapted.Through graph-base image segmentation,the possible regions of the objects in the image can be proposed,and then these regions which are treated as regions of interest are classified as true target or false target.Experimental results verify the effectiveness of the presented method.(3)Traditional PCA-HOG based pedestrian head detection algorithm has the shortcomings of less of the discriminative ability in the subspace.For that the PCA algorithm does not take into account whether the sample belongs to positive group or negative group.In order to handle this problem,The PCA algorithm is divided into two steps which carry out on positive sample and negative sample separately.After this,the HOG feature and the LBP feature are fused to get the final feature descriptor.The results show that the proposed method can effectively improve the detection accuracy compared with the traditional HOG operator.(4)The software architecture of pedestrian head detection system is built.In order to meet the actual needs of pedestrian head detection system,the corresponding detection software is developed under the framework of Visual Studio2010+Opencv.The multi-thread technology is used to improve the stability and real-time performance of the software,which implement the image processing unit and the data exchange unit with different threads.Experimental results show that the developed software can run stably.