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

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hongzhi Zhou ◽  
Gan Yu
Keyword(s):  
Neural Network ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Frame Rate ◽  
Original Video ◽  
Video Object ◽  
Sample Weight ◽  
Adaboost Algorithm ◽  
High Level ◽  
Real Traffic

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.

scholarly journals KeratoDetect: Keratoconus Detection Algorithm Using Convolutional Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Alexandru Lavric ◽  
Popa Valentin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Treatment Options ◽  
Diagnostic Errors ◽  
Detection Algorithm ◽  
Rapid Screening ◽  
Test Set ◽  
Corneal Deformation ◽  
Long Time ◽  
High Level

Keratoconus (KTC) is a noninflammatory disorder characterized by progressive thinning, corneal deformation, and scarring of the cornea. The pathological mechanisms of this condition have been investigated for a long time. In recent years, this disease has come to the attention of many research centers because the number of people diagnosed with keratoconus is on the rise. In this context, solutions that facilitate both the diagnostic and treatment options are quickly needed. The main contribution of this paper is the implementation of an algorithm that is able to determine whether an eye is affected or not by keratoconus. The KeratoDetect algorithm analyzes the corneal topography of the eye using a convolutional neural network (CNN) that is able to extract and learn the features of a keratoconus eye. The results show that the KeratoDetect algorithm ensures a high level of performance, obtaining an accuracy of 99.33% on the data test set. KeratoDetect can assist the ophthalmologist in rapid screening of its patients, thus reducing diagnostic errors and facilitating treatment.

scholarly journals Deep Convolutional Neural Network for Pedestrian Detection with Multi-Levels Features Fusion

2018 ◽  
Vol 232 ◽  
pp. 01061
Author(s):  
Danhua Li ◽  
Xiaofeng Di ◽  
Xuan Qu ◽  
Yunfei Zhao ◽  
Honggang Kong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Deep Convolutional Neural Network ◽  
High Quality ◽  
Low Level ◽  
Features Fusion ◽  
Current State ◽  
High Level

Pedestrian detection aims to localize and recognize every pedestrian instance in an image with a bounding box. The current state-of-the-art method is Faster RCNN, which is such a network that uses a region proposal network (RPN) to generate high quality region proposals, while Fast RCNN is used to classifiers extract features into corresponding categories. The contribution of this paper is integrated low-level features and high-level features into a Faster RCNN-based pedestrian detection framework, which efficiently increase the capacity of the feature. Through our experiments, we comprehensively evaluate our framework, on the Caltech pedestrian detection benchmark and our methods achieve state-of-the-art accuracy and present a competitive result on Caltech dataset.

Fast Pedestrian Detection for Intelligent Vehicle Based on FPGA and Monocular Vision

2013 ◽  
Vol 651 ◽  
pp. 784-788
Author(s):  
Xiao Dong Miao ◽  
Shun Ming Li ◽  
Min Xiang Wei ◽  
Huan Shen
Keyword(s):  
Detection System ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Intelligent Vehicle ◽  
Image Generation ◽  
Integral Image ◽  
Verilog Hdl ◽  
Adaboost Algorithm ◽  
Image Scaling ◽  
Computational Speed

This paper presents a fast pedestrian detection algorithm for intelligent vehicle based on FPGA architecture, using AdaBoost algorithm and Haar features. We describe the hardware design including image scaling, integral image generation, pipelined processing as well as classifier, and parallel processing multiple classifiers to accelerate the computational speed of the pedestrian detection system. The proposed architecture for pedestrian detection has been tested using Verilog HDL and implemented in Xilinx Virtex-5 FPGA. Its performance has been measured about 38 times than the equivalent software implementation.

scholarly journals Application of Convolutional Neural Network (CNN)–AdaBoost Algorithm in Pedestrian Detection

2020 ◽  
Vol 32 (6) ◽  
pp. 1997
Author(s):  
Guiyuan Li ◽  
Changfu Zong ◽  
Guangfeng Liu ◽  
Tianjun Zhu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Pedestrian Detection ◽  
Adaboost Algorithm

scholarly journals Pedestrian Detection Algorithm Based on Improved Convolutional Neural Network

2017 ◽  
Vol 21 (5) ◽  
pp. 834-839 ◽  
Author(s):  
Qin Qin ◽  
Josef Vychodil ◽  
◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Feature Detection ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
The Neural Network ◽  
Detection Ratio ◽  
Detection Window ◽  
Better Than

This paper proposes a new multi-feature detection method of local pedestrian based on a convolutional neural network (CNN), which provides a reliable basis for multi-feature fusion in pedestrian detection. According to the standard of pedestrian detection ratio, the pedestrian under the detection window would be segmented, using the sample labels to guide the local characteristics of CNN learning, the supervised learning after the network can obtain the local feature fusion more pedestrian description ability. Finally, a large number of experiments have been performed. The experimental results show that the local features of the neural network are better than those of most pedestrian features and combination features.

scholarly journals The algorithm of nighttime pedestrian detection in intelligent surveillance for renewable energy power stations

2020 ◽  
Vol 38 (5) ◽  
pp. 2019-2036 ◽  
Author(s):  
Bao Peng ◽  
Zhi-Bin Chen ◽  
Erkang Fu ◽  
Zi-Chuan Yi
Keyword(s):  
Support Vector Machine ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Frame Rate ◽  
Support Vector ◽  
False Alarms ◽  
Image Preprocessing ◽  
Intelligent Surveillance ◽  
Power Stations ◽  
Number Of False Alarms

Intelligent surveillance is an important management method for the construction and operation of power stations such as wind power and solar power. The identification and detection of equipment, facilities, personnel, and behaviors of personnel are the key technology for the ubiquitous electricity The Internet of Things. This paper proposes a video solution based on support vector machine and histogram of oriented gradient (HOG) methods for pedestrian safety problems that are common in night driving. First, a series of image preprocessing methods are used to optimize night images and detect lane lines. Second, an image is divided into intelligent regions to be adapted to different road environments. Finally, the HOG and support vector machine methods are used to optimize the pedestrian image on a Linux system, which reduces the number of false alarms in pedestrian detection and the workload of the pedestrian detection algorithm. The test results show that the system can successfully detect pedestrians at night. With image preprocessing optimization, the correct rate of nighttime pedestrian detection can be significantly improved, and the correct rate of detection can reach 92.4%. After the division area is optimized, the number of false alarms decreases significantly, and the average frame rate of the optimized video reaches 28 frames per second.

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