Traditional AdaBoost Algorithm Research Articles

Deep Cascade AdaBoost with Unsupervised Clustering in Autonomous Vehicles

In recent years, deep learning has achieved excellent performance in a growing number of application fields. With the help of high computation and large-scale datasets, deep learning models with huge parameters constantly enhance the performance of traditional algorithms. Additionally, the AdaBoost algorithm, as one of the traditional machine learning algorithms, has a minimal model and performs well on small datasets. However, it is still challenging to select the optimal classification feature template from a large pool of features in any scene quickly and efficiently. Especially in the field of autonomous vehicles, images taken by onboard cameras contain all kinds of targets on the road, which means the images are full of multiple features. In this paper, we propose a novel Deep Cascade AdaBoost model, which effectively combines the unsupervised clustering algorithm based on deep learning and the traditional AdaBoost algorithm. First, we use the unsupervised clustering algorithm to classify the sample data automatically. We can obtain classification subsets with small intra-class and large inter-class errors by specifying positive and negative samples. Next, we design a training framework for Cascade-AdaBoost based on clustering and mathematically demonstrate that our framework has better detection performance than the traditional Cascade-AdaBoost framework. Finally, experiments on the KITTI dataset demonstrate that our model performs better than the traditional Cascade-AdaBoost algorithm in terms of accuracy and time. The detection time was shortened by 30%, and the false detection rate was reduced by 20%. Meanwhile, the training time of our model is significantly shorter than the traditional Cascade-AdaBoost algorithm.

A Study on the Design and Implementation of an Improved AdaBoost Optimization Mathematical Algorithm Based on Recognition of Packaging Bottles

In this paper, a special design system is developed based on the design of the packaging bottle to achieve the effective acquisition of the image of the cross-section of the packaging bottle to be measured under the condition of limited space size, avoiding the distortion of the object to be measured. At the same time, the image of the area where the target packaging bottle is located is segmented, and the curve features are quickly determined and effectively matched with the template library to realize the recognition of the shape features of the bottle. In this paper, the design of the packaging bottle is first designed by mechanism design and 3D modeling, followed by rapid prototyping methods such as 3D printing, and the prototype is made for functional verification. Finally, the transmission speed and stability of the design system for packaging bottle recognition are improved through structural analysis and optimization methods. To realize the intelligent control of the packaging bottle transmission and identification system, the hardware control circuit is designed and the relevant intelligent control program is prepared based on the embedded system so that the packaging bottles in the transmission process can be quickly and accurately positioned and identified. An improved AdaBoost algorithm is proposed for packaging bottle detection. In the process of algorithm learning, the Haar features are too large and time-consuming, and the training sample is cropped to remove the sample edge pixels, which effectively reduces the number of features, thus reducing the computation. The proposed optical flow method is used to obtain the motion region in the video image as the region of interest, and the canny operator is used in the region of interest for edge detection, and the region of interest is filtered by the edge energy to exclude the noninterest region. Finally, the AdaBoost algorithm is used to detect the region of interest, which reduces the detection area and decreases the detection time. The improved AdaBoost algorithm has a high accuracy improvement over the traditional AdaBoost algorithm for the recognition of various packaging bottles with relatively suitable training set samples, and the system recognition time has reached the requirements of industrial recognition.

Research on School Classroom Teaching Model Based on Clustering Algorithm and Fuzzy Control

The method of computational intelligence to monitor and evaluate the concentration of students in the teaching process can promptly and effectively adjust the learning plan and improve the learning effect. In this article, clustering algorithm and fuzzy control methods are used to construct a research model of students’ attention in class. In addition, this article uses the existing MATLAB-based image feature recognition algorithm to detect and obtain facial features and analyze the main features of facial expressions through computational techniques to realize the judgment of attention. In addition, this article optimizes the traditional AdaBoost algorithm to save computing time and improve operating efficiency and system performance stability. Finally, this article constructs the functional modules of the research model according to actual needs and designs experiments to verify the performance of the model. Experimental research results show that the model constructed in this article has a certain effect.

Real-time Monitoring of Fluidized Bed Agglomerating based on Improved Adaboost Algorithm

Absctract: In order to to detect the polymer agglomeration in fluidized bed reactor (FBR), a method of real-time monitoring of agglomeration in fluidized bed polyolefin reactor based on voiceprint feature recognition is developed. First, the acoustic emission detection technology is applied to collect the acoustic signal generated by the polymer collision on the inner wall of FBR. Then, the voiceprint features of the collected acoustic signal are extracted with the Mel Frequency Cepstrum Coefficients (MFCC) and the Linear Prediction Cepstrum Coefficients (LPCC). To classify the extracted voiceprint features, an improved Adaboost algorithm is proposed to establish the real-time agglomeration classification model. Due to the introduction of cost factor and Gini index decision-making calculation to the Adaboost algorithm, the proposed improved Adaboost algorithm can classify unbalanced small samples with better accuracy and F-score index compared with the traditional Adaboost algorithm. The experiment results in a fluidized bed pilot plant have verified the effectiveness and feasibility of the proposed method.

Research on Fast Pedestrian Detection Algorithm Based on Autoencoding Neural Network and AdaBoost

In order to solve the problem of low accuracy of pedestrian detection of real traffic cameras and high missed detection rate of small target pedestrians, this paper combines autoencoding neural network and AdaBoost to construct a fast pedestrian detection algorithm. Aiming at the problem that a single high-level output feature map has insufficient ability to express pedestrian features and existing methods cannot effectively select appropriate multilevel features, this paper improves the traditional AdaBoost algorithm structure, that is, the sample weight update formula and the strong classifier output formula are reset, and the two-input AdaBoost-DBN classification algorithm is proposed. Moreover, in view of the problem that the fusion video is not smoothly played, this paper considers the motion information of the video object, performs pixel interpolation by motion compensation, and restores the frame rate of the original video by reconstructing the dropped interframe image. Through experimental research, we can see that the algorithm constructed in this paper has a certain effect.

Research on Human Movement Target Recognition Algorithm in Complex Traffic Environment

With the increase in the total population of the society and the continuous increase in the number of trips, the traffic pressures faced by people are increasing. With the development and advancement of computer technology, the emergence of intelligent transportation provides a better way to solve the problem of effectively alleviating traffic pressure and reducing the incidence of traffic accidents. In recent years, intelligent traffic monitoring system, as one of the important branches in the field of intelligent transportation, has also received more and more attention. Among them, video-based moving target recognition technology involves theoretical knowledge in various fields such as artificial intelligence, image processing, pattern recognition and computer vision. It is an important means to realize “safe city” and “smart city” and a key technology for intelligent monitoring. Therefore, the research on human motion target recognition algorithm in complex traffic environment has important theoretical and practical value. In the field of intelligent traffic monitoring, the moving target detection and recognition effect of video images will have certain influence on the classification and behavior understanding of subsequent moving targets. In this paper, the commonly used moving target detection methods are studied first, and the convergence problem of the traditional Adaboost algorithm is improved. An Adaboost algorithm based on adaptive weight update is proposed, and then the support vector machine (SVM) is used. The algorithm identifies the detected moving target. Finally, through simulation experiments on the acquired video images, the results show that the proposed human motion target recognition algorithm based on adaptive weight update Adaboost and SVM has good feasibility and rationality.

Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data.

The Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, it is challenging to apply the AdaBoost algorithm directly to imbalanced data since it is designed mainly for processing misclassified samples rather than samples of minority classes. To better process imbalanced data, this paper introduces the indicator Area Under Curve (AUC) which can reflect the comprehensive performance of the model, and proposes an improved AdaBoost algorithm based on AUC (AdaBoost-A) which improves the error calculation performance of the AdaBoost algorithm by comprehensively considering the effects of misclassification probability and AUC. To prevent redundant or useless weak classifiers the traditional AdaBoost algorithm generated from consuming too much system resources, this paper proposes an ensemble algorithm, PSOPD-AdaBoost-A, which can re-initialize parameters to avoid falling into local optimum, and optimize the coefficients of AdaBoost weak classifiers. Experiment results show that the proposed algorithm is effective for processing imbalanced data, especially the data with relatively high imbalances.

Research on Fatigue Driving System Based on OpenCV

This paper proposes a human eye recognition algorithm based on Haar-like feature and adaboost classifier. Aim to solve the problem that traditional human eye detection technology have in low recognition rate and long training time. The first thing to do was to position the face. After the face was positioned successfully, we used the upper half part of the face image to further locate the human eye. Then extracted the haar features and trained human eye classifier by using adaboost algorithm. This experiment shows that method used in the paper has higher recognition rate than the traditional adaboost algorithm. And it can effectively reduces the interference effect of light in the recognition process.

Study on the Method of Face Detection Based on Chaos Genetic Algorithm Optimization AdaBoost Algorithm

Aiming at the problems that the traditional AdaBoost algorithm has complex feature computation, long training time and low detection rate, a method of face detection based on chaos genetic algorithm optimization adaBoost algorithm was proposed. Firstly, this algorithm uses the image color segmentation for coarse screening on the face image, in order to determine the human skin area. Secondly, the adaptive median filtering is applied to denoise the face image to improve the quality of the face image. Finally, the chaotic genetic algorithm is used to optimize the AdaBoost algorithm to achieve higher detection rate and detection speed. Compared with the traditional AdaBoost algorithm, the experimental results showed that the face detection method based on chaos genetic algorithm optimization AdaBoost algorithm proposed in this paper has a significant improvement in detection rate and detection speed.

Research on a Target Object Locating Method

Target detection has become a common problem to be solved in computer field. Face detection is an important research direction in target detection and a key link in face information processing as well as the premise and foundation of automatic face recognition. Face detection property influences the result of face information recognition directly. This paper improves current face detection algorithms according to traditional AdaBoost algorithm and Sa-AdaBoostSVM algorithm. The experimental result shows that the algorithm in this paper has good robustness for multi-pose and multi-expression face.

Adaboost face detection algorithm based on correlation of classifiers

In order to enhance the ensemble ability of the traditional Adaboost algorithm,an improved Adaboost algorithm was proposed,which was based on the correlation of classifiers. In the algorithm,the correlation estimation of classifiers was added in the weak training classifiers. Every weak classifier was related not only to the current classifier,but also to previous classifiers as well. The experimental results in Carnegie Mellon University (CMU) show that the algorithm is of better detection rate and lower false alarm rate,compared with traditional Adaboost algorithm.