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.

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.

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.

Identification of Potential Reusable Subroutines in Recorded Automated Test Scripts

2018 ◽  
Vol 28 (01) ◽  
pp. 3-36
Author(s):  
Miroslav Bures ◽  
Martin Filipsky ◽  
Ivan Jelinek
Keyword(s):  
User Interface ◽  
Source Code ◽  
Automated Testing ◽  
Test Automation ◽  
Specific Test ◽  
Automated Test ◽  
Test Sets ◽  
Automated Process

In the automated testing based on actions in user interface of the tested application, one of the key challenges is maintenance of these tests. The maintenance overhead can be decreased by suitably structuring the test scripts, typically by employing reusable objects. To aid in the development, maintenance and refactoring of these test scripts, potentially reusable objects can be identified by a semi-automated process. In this paper, we propose a solution that identifies the potentially reusable objects in a set of automated test scripts and then provides developers with suggestions about these objects. During this process, we analyze the semantics of specific test steps using a system of abstract signatures. The solution can be used to identify the potentially reusable objects in both recorded automated test sets and tests programmed in an unstructured style. Moreover, compared to approaches that are based solely on searching for repetitive source code fragments, the proposed system identifies potentially reusable objects that are more relevant for test automation.

A New Pedestrian Feature Description Method Named Neighborhood Descriptor of Oriented Gradients

2021 ◽  
Vol 16 (1) ◽  
pp. 23-55
Author(s):  
Qian Liu ◽  
Feng Yang ◽  
XiaoFen Tang
Keyword(s):  
Feature Vector ◽  
Pedestrian Detection ◽  
Weight Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Local Weight ◽  
Detection Approach ◽  
Training Samples ◽  
New Feature ◽  
Linear Svm

In view of the issue of the mechanism for enhancing the neighbourhood relationship of blocks of HOG, this paper proposes neighborhood descriptor of oriented gradients (NDOG), an improved feature descriptor based on HOG, for pedestrian detection. To obtain the NDOG feature vector, the algorithm calculates the local weight vector of the HOG feature descriptor, while integrating spatial correlation among blocks, concatenates this weight vector to the tail of the HOG feature descriptor, and uses the gradient norm to normalize this new feature vector. With the proposed NDOG feature vector along with a linear SVM classifier, this paper develops a complete pedestrian detection approach. Experimental results for the INRIA, Caltech-USA, and ETH pedestrian datasets show that the approach achieves a lower miss rate and a higher average precision compared with HOG and other advanced methods for pedestrian detection especially in the case of insufficient training samples.

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 Humanrecognition Using ‘Pso-Ofa’ In Low Resolution Videos

2021 ◽  
Vol 12 (11) ◽  
pp. 697-703
Author(s):  
K.Ranga Narayana, Et. al.
Keyword(s):  
Optical Flow ◽  
Moving Objects ◽  
Previous Method ◽  
Video Data ◽  
Detection Methods ◽  
Support Vector ◽  
Low Resolution ◽  
Swarm Optimization ◽  
Detection Approach ◽  
Novel Method

In present scenario, tracking of target in videos with low resolution is most important task.  The problem aroused due to lack of discriminatory data that have low visual visibility of the moving objects. However, earlier detection methods often extract explanations around fascinating points of space or exclude mathematical features in moving regions, resulting in limited capabilities to detect better video functions. To overcome the above problem, in this paper a novel method which recognizes a person from low resolution videos is proposed. A Three step process is implemented in which during the first step, the video data acquired from a low-resolution video i.e. from three different datasets. The acquired video is divided into frames and converted into gray scale from RGB. Secondly, background subtraction is performed using LBP and thereafter Histogram of Optical Flow (HOF) descriptors is extracted from optical flow images for motion estimation. In the third step, the eigen features are extracted and optimized using particle swarm optimization (PSO) model to eliminate redundant information and obtain optimized features from the video which is being processed. Finally to find a person from low resolution videos, the features are classified by Support Vector Machine (SVM) and parameters are evaluated. Experimental results are performed on VIRAT, Soccer and KTH datasets and demonstrated that the proposed detection approach is superior to the previous method

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