A Low-Cost Monocular Vision-Based Obstacle Avoidance Using SVM and Optical Flow

2018 ◽  
Vol 06 (04) ◽  
pp. 267-275
Author(s):  
Ajay Shankar ◽  
Mayank Vatsa ◽  
P. B. Sujit
Keyword(s):  
Optical Flow ◽  
Autonomous Navigation ◽  
Low Cost ◽  
Obstacle Detection ◽  
Monocular Vision ◽  
Support Vector ◽  
Svm Classifier ◽  
Range Finding ◽  
Image Patches ◽  
Computational Resources

Development of low-cost robots with the capability to detect and avoid obstacles along their path is essential for autonomous navigation. These robots have limited computational resources and payload capacity. Further, existing direct range-finding methods have the trade-off of complexity against range. In this paper, we propose a vision-based system for obstacle detection which is lightweight and useful for low-cost robots. Currently, monocular vision approaches used in the literature suffer from various environmental constraints such as texture and color. To mitigate these limitations, a novel algorithm is proposed, termed as Pyramid Histogram of Oriented Optical Flow ([Formula: see text]-HOOF), which distinctly captures motion vectors from local image patches and provides a robust descriptor capable of discriminating obstacles from nonobstacles. A support vector machine (SVM) classifier that uses [Formula: see text]-HOOF for real-time obstacle classification is utilized. To avoid obstacles, a behavior-based collision avoidance mechanism is designed that updates the probability of encountering an obstacle while navigating. The proposed approach depends only on the relative motion of the robot with respect to its surroundings, and therefore is suitable for both indoor and outdoor applications and has been validated through simulated and hardware experiments.

scholarly journals Classification of Action Based Video using Heterogeneous Feature Extraction and SVM

2019 ◽  
Vol 8 (11) ◽  
pp. 1887-1892
Keyword(s):  
Optical Flow ◽  
Video Sequence ◽  
Human Action ◽  
Video Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Video Frames ◽  
Integral Role ◽  
Heterogeneous Feature

Action recognition (AR) plays a fundamental role in computer vision and video analysis. We are witnessing an astronomical increase of video data on the web and it is difficult to recognize the action in video due to different view point of camera. For AR in video sequence, it depends upon appearance in frame and optical flow in frames of video. In video spatial and temporal components of video frames features play integral role for better classification of action in videos. In the proposed system, RGB frames and optical flow frames are used for AR with the help of Convolutional Neural Network (CNN) pre-trained model Alex-Net extract features from fc7 layer. Support vector machine (SVM) classifier is used for the classification of AR in videos. For classification purpose, HMDB51 dataset have been used which includes 51 Classes of human action. The dataset is divided into 51 action categories. Using SVM classifier, extracted features are used for classification and achieved best result 95.6% accuracy as compared to other techniques of the state-of- art.v

scholarly journals An Improved Micro-Expression Recognition Method Based on Necessary Morphological Patches

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 497 ◽  
Author(s):  
Yue Zhao ◽  
Jiancheng Xu
Keyword(s):  
Optical Flow ◽  
Support Vector ◽  
Svm Classifier ◽  
Expression Recognition ◽  
Optical Flow Method ◽  
Psychological Analysis ◽  
Emotional Representation ◽  
Discriminant Ability ◽  
Casme Ii ◽  
Micro Expression

Micro-expression is a spontaneous emotional representation that is not controlled by logic. A micro-expression is both transitory (short duration) and subtle (small intensity), so it is difficult to detect in people. Micro-expression detection is widely used in the fields of psychological analysis, criminal justice and human-computer interaction. Additionally, like traditional facial expressions, micro-expressions also have local muscle movement. Psychologists have shown micro-expressions have necessary morphological patches (NMPs), which are triggered by emotion. Furthermore, the objective of this paper is to sort and filter these NMPs and extract features from NMPs to train classifiers to recognize micro-expressions. Firstly, we use the optical flow method to compare the on-set frame and the apex frame of the micro-expression sequences. By doing this, we could find facial active patches. Secondly, to find the NMPs of micro-expressions, this study calculates the local binary pattern from three orthogonal planes (LBP-TOP) operators and cascades them with optical flow histograms to form the fusion features of the active patches. Finally, a random forest feature selection (RFFS) algorithm is used to identify the NMPs and to characterize them via support vector machine (SVM) classifier. We evaluated the proposed method on two popular publicly available databases: CASME II and SMIC. Results show that NMPs are statistically determined and contribute to significant discriminant ability instead of holistic utilization of all facial regions.

Optical Flow-Based Monocular Vision/INS Integrated Navigation for Mobile Robot Indoors

2014 ◽  
Vol 538 ◽  
pp. 375-378 ◽  
Author(s):  
Xi Yuan Chen ◽  
Jing Peng Gao ◽  
Yuan Xu ◽  
Qing Hua Li
Keyword(s):  
Mobile Robot ◽  
Optical Flow ◽  
Global Positioning System ◽  
Autonomous Navigation ◽  
Image Sequences ◽  
Velocity Estimation ◽  
Monocular Vision ◽  
Integrated Navigation ◽  
Global Positioning ◽  
Flow Algorithm

This paper proposed a new algorithm for optical flow-based monocular vision (MV)/ inertial navigation system (INS) integrated navigation. In this mode, a downward-looking camera is used to get the image sequences, which is used to estimate the velocity of the mobile robot by using optical flow algorithm. INS is employed for the yaw variation. In order to evaluate the performance of the proposed method, a real indoor test has done. The result shows that the proposed method has good performance for velocity estimation. It can be applied to the autonomous navigation of mobile robots when the Global Positioning System (GPS) and code wheel is unavailable.

People Counting Based on the Moving Feature of the Mass

2014 ◽  
Vol 989-994 ◽  
pp. 2540-2542
Author(s):  
Peng Zhe Qiao ◽  
Tao Li ◽  
Tao Xiang ◽  
Xi Zhi Zhang
Keyword(s):  
Support Vector Machine ◽  
Energy Density ◽  
Optical Flow ◽  
Video Surveillance ◽  
Support Vector ◽  
Svm Classifier ◽  
People Counting ◽  
The People ◽  
Flow Algorithm ◽  
Counting Model

In order to improve the accuracy of people counting in video surveillance, the method for people counting based on the moving feature of the mass is proposed. We obtain the orientation and energy density of mass through the optical flow algorithm, and get the information about the size of mass to design the feature of mass. The people counting model is obtained by training a support vector machine (SVM) classifier with the moving feature and shape feature of mass. The experimental results confirm that our approach improves the accuracy of people counting.

scholarly journals Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4335
Author(s):  
Goran Šeketa ◽  
Lovro Pavlaković ◽  
Dominik Džaja ◽  
Igor Lacković ◽  
Ratko Magjarević
Keyword(s):  
Large Scale ◽  
Detection System ◽  
Low Cost ◽  
Fall Detection ◽  
Support Vector ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Data Segmentation ◽  
Post Impact

Automatic fall detection systems ensure that elderly people get prompt assistance after experiencing a fall. Fall detection systems based on accelerometer measurements are widely used because of their portability and low cost. However, the ability of these systems to differentiate falls from Activities of Daily Living (ADL) is still not acceptable for everyday usage at a large scale. More work is still needed to raise the performance of these systems. In our research, we explored an essential but often neglected part of accelerometer-based fall detection systems—data segmentation. The aim of our work was to explore how different configurations of windows for data segmentation affect detection accuracy of a fall detection system and to find the best-performing configuration. For this purpose, we designed a testing environment for fall detection based on a Support Vector Machine (SVM) classifier and evaluated the influence of the number and duration of segmentation windows on the overall detection accuracy. Thereby, an event-centered approach for data segmentation was used, where windows are set relative to a potential fall event detected in the input data. Fall and ADL data records from three publicly available datasets were utilized for the test. We found that a configuration of three sequential windows (pre-impact, impact, and post-impact) provided the highest detection accuracy on all three datasets. The best results were obtained when either a 0.5 s or a 1 s long impact window was used, combined with pre- and post-impact windows of 3.5 s or 3.75 s.

scholarly journals From pixels to patches: a cloud classification method based on bag of micro-structures

2015 ◽  
Vol 8 (10) ◽  
pp. 10213-10247 ◽  
Author(s):  
Q. Li ◽  
Z. Zhang ◽  
W. Lu ◽  
J. Yang ◽  
Y. Ma ◽  
...  
Keyword(s):  
Image Representation ◽  
Feature Space ◽  
Classification Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Clear Sky ◽  
Cloud Classification ◽  
Image Patches ◽  
Sky Conditions ◽  
Micro Structures

Abstract. Automatic cloud classification has attracted more and more attention with the increasing development of whole sky imagers, but it is still in progress for ground-based cloud observation. This paper proposes a new cloud classification method, named bag of micro-structures (BoMS). This method treats an all-sky image as a collection of micro-structures mapped from image patches, rather than a collection of pixels. And then it constructs an image representation with a weighted histogram of micro-structures. Lastly, a support vector machine (SVM) classifier is applied on the image representation because SVM is appealing for sparse and high dimensional feature space. Five different sky conditions are identified: cirriform, cumuliform, stratiform, clear sky and mixed cloudiness that often appears in all-sky images but is seldom addressed in literature. BoMS is evaluated on a large dataset, which contains 5000 all-sky images that are captured by a total-sky cloud imager located in Tibet (29.25° N, 88.88° E). BoMS achieves an accuracy of 90.9 % for 10 fold cross-validation, and it outperforms the state-of-the-art method with an increase of about 19 %. Furthermore, influence of key parameters in BoMS are investigated to verify their robustness.

scholarly journals Comparison between low-cost passive and active vision for obstacle depth

2020 ◽  
Vol 6 ◽  
Author(s):  
Thiago Rateke ◽  
Vito Francisco Chiarella ◽  
Karla Aparecida Justen ◽  
Antonio Carlos Sobieranski ◽  
Sylvio Luiz Mantelli Neto ◽  
...  
Keyword(s):  
Autonomous Navigation ◽  
Low Cost ◽  
Active Vision ◽  
Obstacle Detection ◽  
Disparity Map ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Passive Devices ◽  
Localization And Mapping ◽  
Better Than

Obstacle detection is a key issue in many current applications, especially in applications that have been increasingly highlighted such as: advanced driver assistance systems (ADAS), simultaneous localization and mapping (SLAM) and autonomous navigation system. This can be achieved by active and passive acquisition vision systems, for example: laser and cameras respectively. In this paper we present a comparison between low-cost active and passive devices, more specifically LIDAR and two cameras. To this comparison a disparity map is created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures). The obtained results shown that passive vision can be as good as or even better than active vision in low cost scenarios.

Real-Time EEG Device for Epilepsy Detection Using Wavelet Transform and Support Vector Machine

2021 ◽  
pp. 104-135
Author(s):  
Sharad Sarjerao Jagtap ◽  
Rajesh Kumar M.
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Real Time ◽  
Low Cost ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Feature Extraction Technique

This chapter gives an effective and efficient technique that can detect epilepsy in real time. It is low cost, low power, and real-time devices that can easily detect epilepsy. Along with EEG device, one can upgrade with GSM module to alert the doctors and parents of patients about its occurrence to prevent a sudden fall, which may cause injury and death. The accuracy of this EEG device depends on the quality of feature extraction technique and classification algorithm. In this chapter, support vector machine (SVM) is used as a classifier. Wavelet transform gives feature extraction, which helps to train data and to detect normal or seizure patients. Discrete wavelet transform (DWT) decomposes the signals into three decomposition levels. In this detection, mean, median, and non-linear parameter entropy were calculated for every sub-band as key parameters. The extracted features are then applied to SVM classifier for the classification. Better accuracy of classification is obtained using wavelet and SVM classifier.

COVID-Safe Spatial Occupancy Monitoring Using OFDM-Based Features and Passive WiFi Samples

2021 ◽  
Vol 12 (4) ◽  
pp. 1-24
Author(s):  
Junye Li ◽  
Aryan Sharma ◽  
Deepak Mishra ◽  
Gustavo Batista ◽  
Aruna Seneviratne
Keyword(s):  
Learning Algorithm ◽  
Low Cost ◽  
Support Vector ◽  
Svm Classifier ◽  
Accuracy Rate ◽  
Channel State ◽  
State Information ◽  
Social Distancing ◽  
Occupancy Monitoring ◽  
Safe Limit

During the COVID-19 pandemic, authorities have been asking for social distancing to prevent transmission of the virus. However, enforcing such distancing has been challenging in tight spaces such as elevators and unmonitored commercial settings such as offices. This article addresses this gap by proposing a low-cost and non-intrusive method for monitoring social distancing within a given space, using Channel State Information (CSI) from passive WiFi sensing. By exploiting the frequency selective behavior of CSI with a Support Vector Machine (SVM) classifier, we achieve an improvement in accuracy over existing crowd counting works. Our system counts the number of occupants with a 93% accuracy rate in an elevator setting and predicts whether the COVID-Safe limit is breached with a 97% accuracy rate. We also demonstrate the occupant counting capability of the system in a commercial office setting, achieving 97% accuracy. Our proposed occupancy monitoring outperforms existing methods by at least 7%. Overall, the proposed framework is inexpensive, requiring only one device that passively collects data and a lightweight supervised learning algorithm for prediction. Our lightweight model and accuracy improvements are necessary contributions for WiFi-based counting to be suitable for COVID-specific applications.

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