An Improved Pedestrian Detection Method Based on Adaboost and Performance Analysis

2011 ◽  
Vol 317-319 ◽  
pp. 877-880
Author(s):  
Yan Xi Zhang ◽  
Xiang Dong Gao
Keyword(s):  
Computer Vision ◽  
Detection Method ◽  
Pedestrian Detection ◽  
Intelligent Vehicles ◽  
Sample Weight ◽  
Sliding Windows ◽  
Adaboost Algorithm ◽  
Detection Approach ◽  
And Performance ◽  
Advanced Robotics

Pedestrian detection, which has a wide application in surveillance, advanced robotics, and especially intelligent vehicles, is an important area in computer vision. This paper applies a detection approach based on improved Adaboost algorithm. We use a dataset to train the weak classifiers (with different numbers) to cascade to be strong classifiers, in which we employ optimized strategy of sample weight adjustment to reduce the over-fit. After constructing a strong classifier, we apply different scale of sliding widow to shift and calculate the corresponding features to classify them as pedestrians or non-pedestrians. The experiments show that different numbers of weak classifiers layer and different scale of sliding windows can give different performance in detecting.

Research and Performance Analysis on Pedestrian Detection Based on Optimized Hog and SVM

2014 ◽  
Vol 511-512 ◽  
pp. 247-250 ◽  
Author(s):  
Yan Xi Zhang ◽  
Kai Ping Feng
Keyword(s):  
Detection System ◽  
Pedestrian Detection ◽  
Intelligent Vehicles ◽  
Training Set ◽  
Sliding Windows ◽  
Intelligent Robots ◽  
Histograms Of Oriented Gradients ◽  
Overall Performance ◽  
And Performance ◽  
Linear Svm

Pedestrian detection based on images is one key technology of intelligent vehicles, and it is also widely applied in intelligent robots, intelligent surveillance. This paper mainly focuses on implementing a pedestrian detection system, which is classified by linear SVM with optimized Hog (Histograms of Oriented Gradients) as the extracted features. Then some experiments were done to find out that how the changing resolution of training set, times of bootstrapping iterations and different size and steps of the sliding windows affect the overall performance of detecting systems.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

A Novel Fast Pedestrian Detection Method

2015 ◽  
Vol 738-739 ◽  
pp. 538-541
Author(s):  
Fu Qiang Zhou ◽  
Yan Li
Keyword(s):  
Video Streaming ◽  
Local Binary Pattern ◽  
Detection Method ◽  
Pedestrian Detection ◽  
Original Method ◽  
Histograms Of Oriented Gradients ◽  
Detection Approach

This paper presents novel pedestrian detection approach in video streaming, which could process frames rapidly. The method is based on cascades of HOG-LBP (Histograms of Oriented Gradients-Local Binary Pattern), but combines non-negative factorization to reduce the length of the feature, aiming at realizing a more efficient way of detection, remedying the slowness of the original method. Experiments show our method can process faster than HOG and HOG-LBP, and more accurate than HOG, which has better performance in pedestrian detection in video streaming.

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 Research on Pedestrian’s Detection Based on the Integration of AdaBoost Algorithm and Shape Features

2011 ◽  
Vol 2-3 ◽  
pp. 433-438
Author(s):  
Ying Yang ◽  
Yu Gang Ma ◽  
Xiao Dong Guo ◽  
Kun Jiao
Keyword(s):  
Detection Method ◽  
Pedestrian Detection ◽  
Morphological Characteristics ◽  
Vertical Axis ◽  
Adaptive Threshold ◽  
Candidate Region ◽  
Vertical Edge ◽  
Shape Features ◽  
Adaboost Algorithm ◽  
Axis Of Symmetry

In this Paper, Propose a Pedestrian Detection Method that Based on Adaboost Algorithm and Pedestrian Shape Features Integration. First According to the Collected Pedestrian True, False Sample, Selected the Characteristics of the Extended Class Haar, Adopt Adaboost Algorithm Training Get Pedestrian Classifier to Split the Initial Candidate Region of All Pedestrians in the Image. in this Paper, Propose an Adaptive Threshold Weight Update Method, Significantly Reduced the Number of the Characteristics of Strong Classifier, Optimize the Classifier Structure, Reduce the Complexity of the Algorithm; Meanwhile, the Online Update Detector, Improving the Reliability of the Detector. Pedestrian Leg Have Strong Vertical Edge Symmetry Characteristic so that Extracted the Vertical Edge Detection in the Initial Candidate Region, According to the Symmetry Determine the Vertical Axis of Symmetry, Combined with the Morphological Characteristics of Pedestrians to Determine the Width and Height Characteristics of the Pedestrian, to Determine the Pedestrian Candidate Region, Finally, Put a Further Validation to the Pedestrian Candidate Region.

scholarly journals Detecting Multi-Resolution Pedestrians Using Group Cost-Sensitive Boosting with Channel Features

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 780 ◽  
Author(s):  
Chao Zhu ◽  
Xu-Cheng Yin
Keyword(s):  
Pedestrian Detection ◽  
Low Resolution ◽  
Specific Test ◽  
Adaboost Algorithm ◽  
Detection Approach ◽  
Major Bottleneck ◽  
Boosting Algorithm ◽  
Test Sets ◽  
Late Stages ◽  
Possible Cause

Significant progress has been achieved in the past few years for the challenging task of pedestrian detection. Nevertheless, a major bottleneck of existing state-of-the-art approaches lies in a great drop in performance with reducing resolutions of the detected targets. For the boosting-based detectors which are popular in pedestrian detection literature, a possible cause for this drop is that in their boosting training process, low-resolution samples, which are usually more difficult to be detected due to the missing details, are still treated equally importantly as high-resolution samples, resulting in the false negatives since they are more easily rejected in the early stages and can hardly be recovered in the late stages. To address this problem, we propose in this paper a robust multi-resolution detection approach with a novel group cost-sensitive boosting algorithm, which is derived from the standard AdaBoost algorithm to further explore different costs for different resolution groups of the samples in the boosting process, and to place greater emphasis on low-resolution groups in order to better handle the detection of multi-resolution targets. The effectiveness of the proposed approach is evaluated on the Caltech pedestrian benchmark and KAIST (Korea Advanced Institute of Science and Technology) multispectral pedestrian benchmark, and validated by its promising performance on different resolution-specific test sets of both benchmarks.

scholarly journals Body Parts Features-Based Pedestrian Detection for Active Pedestrian Protection System

2016 ◽  
Vol 28 (2) ◽  
pp. 133-142 ◽  
Author(s):  
Lie Guo ◽  
Mingheng Zhang ◽  
Linhui Li ◽  
Yibing Zhao ◽  
Yingzi Lin
Keyword(s):  
Detection Rate ◽  
Detection Method ◽  
Detection System ◽  
Pedestrian Detection ◽  
Urban Traffic ◽  
Body Parts ◽  
Detection Time ◽  
Pedestrian Protection ◽  
Adaboost Algorithm ◽  
Threshold Adjustment

A novel pedestrian detection system based on vision in urban traffic situations is presented to help the driver perceive the pedestrian ahead of the vehicle. To enhance the accuracy and to decrease the time spent on pedestrian detection in such complicated situations, the pedestrian is detected by dividing their body into several parts according to their corresponding features in the image. The candidate pedestrian leg is segmented based on the gentle AdaBoost algorithm by training the optimized histogram of gradient features. The candidate pedestrian head is located by matching the pedestrian head and shoulder model above the region of the candidate leg. Then the candidate leg, head and shoulder are combined by parts constraint and threshold adjustment to verify the existence of the pedestrian. Finally, the experiments in real urban traffic circumstances were conducted. The results show that the proposed pedestrian detection method can achieve pedestrian detection rate of 92.1% with the average detection time of 0.2257 s.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

scholarly journals A Robust Thermal Infrared Vehicle and Pedestrian Detection Method in Complex Scenes

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1240
Author(s):  
Yang Liu ◽  
Hailong Su ◽  
Cao Zeng ◽  
Xiaoli Li
Keyword(s):  
Detection Method ◽  
Selection Process ◽  
Pedestrian Detection ◽  
Thermal Infrared ◽  
Single Shot ◽  
Detection Algorithms ◽  
Complex Scenes ◽  
Thermal Infrared Images ◽  
Online Feature Selection ◽  
Huge Challenge

In complex scenes, it is a huge challenge to accurately detect motion-blurred, tiny, and dense objects in the thermal infrared images. To solve this problem, robust thermal infrared vehicle and pedestrian detection method is proposed in this paper. An important weight parameter β is first proposed to reconstruct the loss function of the feature selective anchor-free (FSAF) module in its online feature selection process, and the FSAF module is optimized to enhance the detection performance of motion-blurred objects. The proposal of parameter β provides an effective solution to the challenge of motion-blurred object detection. Then, the optimized anchor-free branches of the FSAF module are plugged into the YOLOv3 single-shot detector and work jointly with the anchor-based branches of the YOLOv3 detector in both training and inference, which efficiently improves the detection precision of the detector for tiny and dense objects. Experimental results show that the method proposed is superior to other typical thermal infrared vehicle and pedestrian detection algorithms due to 72.2% mean average precision (mAP).

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