Study On Technology Of Pedestrian Detection Based On Panoramic Vision

Nowadays, pedestrian detection is intensively investigated and becoming a hot topic in the fields of computer vision and artificial intelligence. It's also an important branch of object detection. It could be widely used in the military field,intelligent transportation systems,robot navigation, intelligent surveillance, human motion analysis and so on. Detecting human in images is a challenging task because of the variability in clothing and illumination conditions, and the wide range of poses that people can adopt。Nowadays, pedestrian detection is considered as a pattern classification problem in the filed of computer vision. The Algorithm mainly based on machine learning, which extracts features from pedestrian samples and distinguish pedestrian from other targets and background area, to find the accurate location of pedestrian.Panoramic vision system has a large field of view.It's widely used in robot navigation, space probe,video surveillance, virtual reality, environment sensing technology and so on. The combination of pedestrian detection technology and panoramic vision system could play their respective advantages.It's potential application is very promising.Pedestrian detection based on panoramic vision system could take advantage of a large panoramic field of view and detect pedestrian in the 360-degree field of view, to better meet needs of robot navigation intelligent surveillance and other applications. Last few years, Graphics processing unit and general-purpose computing on GPU has been in rapid development,general-purpose computing on graphics processing units has been widely used in computer vision.This paper mainly studies the pedestrian detection algorithm based on panoramic vision system, designs a pedestrian detector based on panoramic vision with integral histograms of oriented gradient features and linear support vector machine, and improves the key steps of the pedestrian detection algorithm with parallel computing technology on GPU platform, achieves significant improvement on the detection speed, while maintaining the premise of detection accuracy。Firstly, by researching the imaging principle of panoramic vision, derive mathematical model of the hyperboloid reflection imaging system. In order to overcome the impact of panoramic image distortion on pedestrian detection,a cylinder unwarping algorithm based on geometry principle of panoramic image is proposed can obtain 360-dcgrce panoramic image. Ensure the detection accuracy of pedestrian detection in panoramic image.Secondly, this paper designs a pedestrian detector based on panoramic vision, based on histogram of oriented gradient features proposed by Dalal, Introduced with the integral image method to calculate the HOG feature set, greatly improved the calculation speed of HOG features. At the same time using a linear support vector machine as a pedestrian classifier. In the pedestrian detection, there are a large number of overlapping windows, we use non-maxima suppression algorithm for integration of multiple results,to obtain better detection results.Finally, this paper improved the pedestrian detector based on panoramic vision on GPU platform,using CUDA parallel computing technology to accelerate to obtain panoramic image, calculate the integral histogram of oriented gradient and the linear support vector machine detection. we detailed analyzes the processing times and the occupancy of each kernel used by our algorithm. Experimental results show that our method achieves significant improvement on the detection speed in panoramic image, and we report a speed up by a factor of 8 over our CPU implementation, while maintaining the premise of detection accuracy.