Research On Multi-Target Pedestrian Detection Method Based On Hough Forest

The research of object detection and tracking in images and videos sequence has become in computer vision field. The use of Object detection and tracking is involved in the applications of the following: video surveillance, human-computer interaction, intelligent retrieval, vehicle navigation and so on. Pedestrian detection of this paper, is an important branch of object detection in the domain of object recognition, which is aimed at detecting, separating and tracking from single image and sequential video frame. In recent years, pedestrian detection has attached more importance of researchers because of its widespread application and deeply important value. But pedestrian detection still has some difficult in precise recognition which is needed to be solved, such as the indeterminacy of environment and the discrepancy of each pedestrian.The traditional method for pedestrian detection is divided into two categories: a method based on the background modeling and a method based on the statistical learning algorithm. The former is not used due to the low robustness and bad antijamming capability. Therefore, most of method for pedestrian detection is based on machine learning. This algorithm first needs to prepare training samples for extracting the features of images. Then the features would be put into the classifier to learn. Finally, we use the learning classifier to detect pedestrians of test images. This paper does some researches on different methods for pedestrian detection and builds a multi-target pedestrian detection system based on modified Hough forest. The main objective of this paper is to solve pedestrians detection and tracking under complicated environment. The main works of this paper are las below:1) Research the method for object detection based on the statistical learning. The method mainly consists of two parts: feature extraction and classifier learning.2) The paper introduces and analyzes four descriptors of feature including HOG, LBP, SIFT and Haar-Like, and researches on different machine learning algorithms as pedestrian classifiers such as SVM algorithm, boosting algorithms, neural network algorithms, etc. We proposes a new pedestrian descriptor combined of Hog feature and Haar-Like feature. Finally, we use Hough forest algorithm as the pedestrian classifier with complex feature because Hough forest can fuse more features to distinguish different categories. The Hough forest can learn and choose the best pedestrian features to form an improved multi-objective pedestrian detection algorithm.3) The original Hough forest algorithm can realize the pedestrian detection in single environment, but it does not apply to complex environment due to limitations of its voting mechanism and raw voting results. Therefore, this paper proposes an optimization algorithm based on Caussian filter area and weight learning to address the limitations of voting module, and adopts a window fusion strategy to process the results, which are together to achieve a lifting detection accuracy.In this paper, we use INRIA dataset and TUD dataset as experimental samples to involve in training and learning module of pedestrian detection system. The results of experiment show that the method proposed in this paper could represent the global information of pedestrians more sufficiently, increase the accuracy of pedestrian detection and satisfy the requirements of real-time detection. And it can get a better ROC performance compared to HOG +SVM algorithm and original Hough forest algorithm.