scholarly journals Implementing Cascade Classifier In Android Application “Find Marks”

2018 ◽  
Vol 3 (01) ◽  
Author(s):  
R.B Wahyu ◽  
Hidayat Saputra
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Training Image ◽  
Android Application ◽  
Cascade Classifier ◽  
Know How ◽  
To Receive

<p>Nowadays going through other places is easier to do. Explore new area is quite common activities by every people since they have sense to know about the environment or situation around them or just curiosity about somewhere else they didn’t know yet. People have different ability to adapt and ways to know how the environment around them. Not all people feeling easy with their new environment and socialize with local people.This research intends to implement object detection as one of feature from computer vision in android application. This application also will assist users how to training image for cascade classifier. User will be able to do object detection using built in Android smartphone camera to receive direction to some place or building from provided marker or logo. It also gives user information about the current place.<em></em></p>

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

scholarly journals A computer vision-based object detection and counting for COVID-19 protocol compliance: a case study of Jakarta

2020 ◽  
Author(s):  
Muhammad Lanang Afkaar Ar ◽  
Sulthan Muzakki Adytia S ◽  
Yudhistira Nugraha ◽  
Farizah Rizka R ◽  
Andy Ernesto ◽  
...  
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Detection And Counting ◽  
Protocol Compliance

scholarly journals A Hard Example Mining Approach for Concealed Multi-Object Detection of Active Terahertz Image

2021 ◽  
Vol 11 (23) ◽  
pp. 11241
Author(s):  
Ling Li ◽  
Fei Xue ◽  
Dong Liang ◽  
Xiaofei Chen
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
State Of The Art ◽  
Terahertz Imaging ◽  
Public Security ◽  
Counter Terrorism ◽  
Detection Algorithms ◽  
Public Dataset ◽  
The One ◽  
Objects Detection

Concealed objects detection in terahertz imaging is an urgent need for public security and counter-terrorism. So far, there is no public terahertz imaging dataset for the evaluation of objects detection algorithms. This paper provides a public dataset for evaluating multi-object detection algorithms in active terahertz imaging. Due to high sample similarity and poor imaging quality, object detection on this dataset is much more difficult than on those commonly used public object detection datasets in the computer vision field. Since the traditional hard example mining approach is designed based on the two-stage detector and cannot be directly applied to the one-stage detector, this paper designs an image-based Hard Example Mining (HEM) scheme based on RetinaNet. Several state-of-the-art detectors, including YOLOv3, YOLOv4, FRCN-OHEM, and RetinaNet, are evaluated on this dataset. Experimental results show that the RetinaNet achieves the best mAP and HEM further enhances the performance of the model. The parameters affecting the detection metrics of individual images are summarized and analyzed in the experiments.

scholarly journals Vanhuksen oikeus hoivaan ja omaisolettama

Gerontologia ◽  
2017 ◽  
Vol 31 (3) ◽  
pp. 227-242
Author(s):  
Laura Kalliomaa-Puha
Keyword(s):  
Family Members ◽  
Elderly Person ◽  
Social Health ◽  
Legal Responsibility ◽  
Public Authorities ◽  
The Public ◽  
Know How ◽  
The Family ◽  
The Right ◽  
To Receive

Jokaisella vanhuksella on Suomessa yksilöllinen, viime kädessä perustuslaissa taattu, oikeus riittävään hoivaan ja huolenpitoon. Silti tämä oikeus on usein käytännössä riippuvainen siitä, onko vanhalla ihmisellä omaisia tukenaan. Tässä artikkelissa tarkastellaan sitä, miten oikeus hoivaan ja hoitoon taataan lainsäädännössä. Omaisilla ei lain mukaan ole vastuuta hoivan järjestämisestä, mutta silti lainsäädäntö monessa kohdin ikään kuin olettaa omaisten olevan vanhuksen tukena. Vaikka omaiset usein ovatkin tukena, miten perusoikeus hoivaan ja huolenpitoon toteutuu niillä vanhuksilla, joilla ei ole omaisia? Artikkeli nostaa vakavimpana omaisolettaman riskinä esiin ne vanhukset, joilla on omaisia, mutta joiden omaiset eivät osaa tai halua auttaa. Right to care and presumption of family and friends in the Finnish legislation According to Finnish legislation the public authorities must guarantee adequate social, health and medical services for those old persons who cannot obtain means necessary for a life of dignity. Yet in practice this right to receive indispensable subsistence and care often depends on the fact whether the old person happens to have family or friends to help her or him. As if the legislation supposes there are friends and family to help, even though, according to Finnish law, family members do not have legal responsibility to take care of an elderly person. This article elaborates how the right to care is guaranteed in Finnish legislation and what the law says about the responsibilities of the family. Even though most of the relatives do help their elderlies, how is the right to care fulfilled for those old persons who do not have family? Perhaps the elderlies who have family and friends, which do not help or do not know how to, are in the most vulnerable situation.

scholarly journals TCM: Temporal Consistency Model for Head Detection in Complex Videos

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Sultan Daud Khan ◽  
Ahmed B. Altamimi ◽  
Mohib Ullah ◽  
Habib Ullah ◽  
Faouzi Alaya Cheikh
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Real World ◽  
Experimental Evaluation ◽  
Research Problem ◽  
Temporal Consistency ◽  
Single Image ◽  
Average Precision ◽  
Consistency Model ◽  
Head Detection

Head detection in real-world videos is a classical research problem in computer vision. Head detection in videos is challenging than in a single image due to many nuisances that are commonly observed in natural videos, including arbitrary poses, appearances, and scales. Generally, head detection is treated as a particular case of object detection in a single image. However, the performance of object detectors deteriorates in unconstrained videos. In this paper, we propose a temporal consistency model (TCM) to enhance the performance of a generic object detector by integrating spatial-temporal information that exists among subsequent frames of a particular video. Generally, our model takes detection from a generic detector as input and improves mean average precision (mAP) by recovering missed detection and suppressing false positives. We compare and evaluate the proposed framework on four challenging datasets, i.e., HollywoodHeads, Casablanca, BOSS, and PAMELA. Experimental evaluation shows that the performance is improved by employing the proposed TCM model. We demonstrate both qualitatively and quantitatively that our proposed framework obtains significant improvements over other methods.

scholarly journals Comparative Performance Analysis of Neural Network Real-Time Object Detections in Different Implementations

2020 ◽  
Vol 226 ◽  
pp. 02020
Author(s):  
Alexey V. Stadnik ◽  
Pavel S. Sazhin ◽  
Slavomir Hnatic
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Performance Analysis ◽  
Object Detection ◽  
Real Time ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Comparative Performance

The performance of neural networks is one of the most important topics in the field of computer vision. In this work, we analyze the speed of object detection using the well-known YOLOv3 neural network architecture in different frameworks under different hardware requirements. We obtain results, which allow us to formulate preliminary qualitative conclusions about the feasibility of various hardware scenarios to solve tasks in real-time environments.

scholarly journals Salient Object Detection Techniques in Computer Vision—A Survey

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1174
Author(s):  
Ashish Kumar Gupta ◽  
Ayan Seal ◽  
Mukesh Prasad ◽  
Pritee Khanna
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Large Scale ◽  
Research Work ◽  
Salient Object Detection ◽  
Future Research ◽  
Automatic Identification ◽  
Salient Object ◽  
Detection Techniques

Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.

scholarly journals Methods and Algorithms for Detecting Objects in Video Files

2018 ◽  
Vol 155 ◽  
pp. 01016 ◽  
Author(s):  
Cuong Nguyen The ◽  
Dmitry Shashev
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Traffic Control ◽  
Motion Pictures ◽  
Real Life ◽  
Video File ◽  
Detection And Tracking ◽  
Object Movement ◽  
Wide Range ◽  
Automated Processing

Video files are files that store motion pictures and sounds like in real life. In today's world, the need for automated processing of information in video files is increasing. Automated processing of information has a wide range of application including office/home surveillance cameras, traffic control, sports applications, remote object detection, and others. In particular, detection and tracking of object movement in video file plays an important role. This article describes the methods of detecting objects in video files. Today, this problem in the field of computer vision is being studied worldwide.

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