Design And Implementation Of A Pedestrian Detection Algorithm Using S-GBDT

With the rapid development of artificial intelligence and machine learning recently,the computer vision has entered into a period of prosperity.Pedestrian detection is one of the most important subjects in the computer vision,and it plays an important role in many applications,such as intelligent video surveillance and driverless car etc.In this paper,we concentrate on the challenging pedestrian detection subject,which is actually a binary classification problem.Both the classifier and features are crucial for a successful pedestrian detection algorithm and the principal studies of this paper are as follows:There are many classifiers having been used in pedestrian detection,such as Support Vector Machine,AdaBoost,and Softmax function in convolutional neural networks etc.Though Gradient Boosting Decision Tree(GBDT)is a widely used algorithm in data mining,personalized recommendation,financial forecasting for examples,it has not been used in the pedestrian detection.Thus the first novelty of this paper is to apply gradient boosting decision tree algorithm to the pedestrian detection,where the algorithm named ACF/LDCF+GBDT are designed and then tested on several datasets,including Inria,Caltech and Kitti.The experimental results show that gradient boosting decision tree can suit well for pedestrian detection.The features extracted from convolutional neural networks are abstract and high level descriptions of an input image.The high level descriptions are better at separating the input data,so we take advantage of these features to improve the pedestrian detection algorithm.The region proposal network(RPN)in Faster R-CNN is actually a good pedestrian detector and the classifier following it degrades its performance.Thus we propose the second novelty of this paper that we firstly use RPN to get the region proposals and features,and then adopt a multi-stage training by using GBDT algorithm with the bootstrapping strategy.Through mining hard negative examples and adding them into the training set gradually,the performance of pedestrian detector gets better and better.In addition,we use the stochastic gradient boosting to speed up the training process and avoid the overfitting.That is to say when training the decision tree model in each stage,a portion of samples and features are randomly selected.In a word,we use stochastic gradient boosting decision tree to train a pedestrian detector with the region proposals and features generated by RPN.Finally,we get a pedestrian detection algorithm combing stochastic gradient boosting decision tree and region proposal networks.We run several experiments on the leading pedestrian detection benchmark Caltech.The experimental results show that our pedestrian detector is very competitive.