scholarly journals Pengenalan Wajah Secara Real Time Menggunakan Metode Camshift dan Operator Erosi Berdasarkan Citra Wajah

2017 ◽  
Vol 1 (1) ◽  
pp. 8
Author(s):  
Sultoni Sultoni ◽  
Rudy Hariyanto
Keyword(s):  
Real Time ◽  
Good Accuracy ◽  
Facial Recognition ◽  
Computation Time ◽  
Research Topic ◽  
Security System ◽  
Fast Computation ◽  
Detection And Tracking ◽  
Tracking Process ◽  
The Future

Facial recognition is a research topic that is quite a lot done by researchers in recent decades, because many benefits in this study such as security system and survelliance. This research is a development or modification of previous research. Where previous research used camhshift method for detection and tracking, in this research is by performing erosion operators on the detection and tracking process using camshift. Based on the results of the test produced a fairly good accuracy and with a fairly fast computation time. So that the future can be applied to enter attendance attendance system or to masku secret room by doing engineering such as merging with microcontroller and so on, so the benefits can be more felt.

scholarly journals Visual MAV Tracker with Adaptive Search Region

2021 ◽  
Vol 11 (16) ◽  
pp. 7741
Author(s):  
Wooryong Park ◽  
Donghee Lee ◽  
Junhak Yi ◽  
Woochul Nam
Keyword(s):  
Real Time ◽  
Computation Time ◽  
Adaptive Strategy ◽  
Adaptive Search ◽  
Fast Computation ◽  
Search Region ◽  
Micro Aerial Vehicle ◽  
Aerial Vehicle ◽  
Online Tracking ◽  
Computation Speed

Tracking a micro aerial vehicle (MAV) is challenging because of its small size and swift motion. A new model was developed by combining compact and adaptive search region (SR). The model can accurately and robustly track MAVs with a fast computation speed. A compact SR, which is slightly larger than a target MAV, is less likely to include a distracting background than a large SR; thus, it can accurately track the MAV. Moreover, the compact SR reduces the computation time because tracking can be conducted with a relatively shallow network. An optimal SR to MAV size ratio was obtained in this study. However, this optimal compact SR causes frequent tracking failures in the presence of the dynamic MAV motion. An adaptive SR is proposed to address this problem; it adaptively changes the location and size of the SR based on the size, location, and velocity of the MAV in the SR. The compact SR without adaptive strategy tracks the MAV with an accuracy of 0.613 and a robustness of 0.086, whereas the compact and adaptive SR has an accuracy of 0.811 and a robustness of 1.0. Moreover, online tracking is accomplished within approximately 400 frames per second, which is significantly faster than the real-time speed.

scholarly journals Human Motion Detection and Tracking for Real-Time Security System

IJARCCE ◽  
2016 ◽  
Vol 5 (12) ◽  
pp. 203-207
Author(s):  
Pavithra S ◽  
Mahanthesh U ◽  
Dr M Shiva kumar
Keyword(s):  
Real Time ◽  
Motion Detection ◽  
Human Motion ◽  
Security System ◽  
Detection And Tracking ◽  
Human Motion Detection

scholarly journals An Asynchronous Real-Time Corner Extraction and Tracking Algorithm for Event Camera

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1475 ◽  
Author(s):  
Jingyun Duo ◽  
Long Zhao
Keyword(s):  
Real Time ◽  
Dynamic Range ◽  
Computation Time ◽  
High Dynamic Range ◽  
Tracking Algorithm ◽  
Corner Detection ◽  
High Temporal Resolution ◽  
Detection And Tracking ◽  
Event Camera ◽  
Event Based

Event cameras have many advantages over conventional frame-based cameras, such as high temporal resolution, low latency and high dynamic range. However, state-of-the-art event- based algorithms either require too much computation time or have poor accuracy performance. In this paper, we propose an asynchronous real-time corner extraction and tracking algorithm for an event camera. Our primary motivation focuses on enhancing the accuracy of corner detection and tracking while ensuring computational efficiency. Firstly, according to the polarities of the events, a simple yet effective filter is applied to construct two restrictive Surface of Active Events (SAEs), named as RSAE+ and RSAE−, which can accurately represent high contrast patterns; meanwhile it filters noises and redundant events. Afterwards, a new coarse-to-fine corner extractor is proposed to extract corner events efficiently and accurately. Finally, a space, time and velocity direction constrained data association method is presented to realize corner event tracking, and we associate a new arriving corner event with the latest active corner that satisfies the velocity direction constraint in its neighborhood. The experiments are run on a standard event camera dataset, and the experimental results indicate that our method achieves excellent corner detection and tracking performance. Moreover, the proposed method can process more than 4.5 million events per second, showing promising potential in real-time computer vision applications.

Real-time bi-directional people counting using an RGB-D camera

Sensor Review ◽  
2021 ◽  
Vol 41 (4) ◽  
pp. 341-349
Author(s):  
Wahyu Rahmaniar ◽  
W.J. Wang ◽  
Chi-Wei Ethan Chiu ◽  
Noorkholis Luthfil Luthfil Hakim
Keyword(s):  
Real Time ◽  
Region Of Interest ◽  
Computation Time ◽  
Depth Image ◽  
Fast Computation ◽  
Content Type ◽  
People Counting ◽  
Rgb Images ◽  
Precision Rate ◽  
Rgb Image

Purpose The purpose of this paper is to propose a new framework and improve a bi-directional people counting technique using an RGB-D camera to obtain accurate results with fast computation time. Therefore, it can be used in real-time applications. Design/methodology/approach First, image calibration is proposed to obtain the ratio and shift values between the depth and the RGB image. In the depth image, a person is detected as foreground by removing the background. Then, the region of interest (ROI) of the detected people is registered based on their location and mapped to an RGB image. Registered people are tracked in RGB images based on the channel and spatial reliability. Finally, people were counted when they crossed the line of interest (LOI) and their displacement distance was more than 2 m. Findings It was found that the proposed people counting method achieves high accuracy with fast computation time to be used in PCs and embedded systems. The precision rate is 99% with a computation time of 35 frames per second (fps) using a PC and 18 fps using the NVIDIA Jetson TX2. Practical implications The precision rate is 99% with a computation time of 35 frames per second (fps) using a PC and 18 fps using the NVIDIA Jetson TX2. Originality/value The proposed method can count the number of people entering and exiting a room at the same time. If the previous systems were limited to only one to two people in a frame, this system can count many people in a frame. In addition, this system can handle some problems in people counting, such as people who are blocked by others, people moving in another direction suddenly, and people who are standing still.

Registration of a hybrid robot using the Degradation-Kronecker method and a purely nonlinear method

Robotica ◽  
2015 ◽  
Vol 34 (12) ◽  
pp. 2729-2740 ◽  
Author(s):  
S. J. Yan ◽  
S. K. Ong ◽  
A. Y. C. Nee
Keyword(s):  
Closed Form ◽  
Real Time ◽  
Good Accuracy ◽  
Tracking System ◽  
Closed Form Solution ◽  
Form Solution ◽  
Fast Computation ◽  
Nonlinear Method ◽  
Simulation And Experiment ◽  
Limited Accuracy

SUMMARYAlthough the registration of a robot is crucial in order to identify its pose with respect to a tracking system, there is no reported solution to address this issue for a hybrid robot. Different from classical registration, the registration of a hybrid robot requires the need to solve an equation with three unknowns where two of these unknowns are coupled together. This property makes it difficult to obtain a closed-form solution. This paper is a first attempt to solve the registration of a hybrid robot. The Degradation-Kronecker (D-K) method is proposed as an optimal closed-form solution for the registration of a hybrid robot in this paper. Since closed-form methods generally suffer from limited accuracy, a purely nonlinear (PN) method is proposed to complement the D-K method. With simulation and experiment results, it has been found that both methods are robust. The PN method is more accurate but slower as compared to the D-K method. The fast computation property of the D-K method makes it appropriate to be applied in real-time circumstances, while the PN method is suitable to be applied where good accuracy is preferred.

Security System and Surveillance Using Real Time Object Tracking and Multiple Cameras

2011 ◽  
Vol 403-408 ◽  
pp. 4968-4973
Author(s):  
Rajendra Kachhava ◽  
Vivek Srivastava ◽  
Rajkumar Jain ◽  
Ekta Chaturvedi
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Real Time ◽  
Video Surveillance ◽  
Research Field ◽  
Security System ◽  
Multiple Cameras ◽  
Multiple Camera ◽  
Detection And Tracking ◽  
Real Time Tracking

In this paper we propose multiple cameras using real time tracking for surveillance and security system. It is extensively used in the research field of computer vision applications, like that video surveillance, authentication systems, robotics, pre-stage of MPEG4 image compression and user inter faces by gestures. The key components of tracking for surveillance system are extracting the feature, background subtraction and identification of extracted object. Video surveillance, object detection and tracking have drawn a successful increased interest in recent years. A object tracking can be understood as the problem of finding the path (i.e. trajectory) and it can be defined as a procedure to identify the different positions of the object in each frame of a video. Based on the previous work on single detection using single stationary camera, we extend the concept to enable the tracking of multiple object detection under multiple camera and also maintain a security based system by multiple camera to track person in indoor environment, to identify by my proposal system which consist of multiple camera to monitor a person. Present study mainly aims to provide security and detect the moving object in real time video sequences and live video streaming. Based on a robust algorithm for human body detection and tracking in videos created with support of multiple cameras.

Design of the Target Tracking Process Based on DM648

2014 ◽  
Vol 599-601 ◽  
pp. 904-907
Author(s):  
Guang Yu Yao ◽  
Lu Song
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Intelligent Transportation System ◽  
Tracking System ◽  
Low Cost ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Tracking Process ◽  
Wide Range ◽  
Fast Processing

Compared with the traditional vehicle detector, the vehicle detection and tracking based on video image processing and the technique of visual target has fast processing speed, and convenient installation and maintenance, and low cost, wide range of monitoring, can obtain more kinds of traffic parameters, and many other advantages, has become more and more widely used in intelligent transportation system (ITS) in recent years. This paper introduces a method for real-time detection, target tracking in traffic image sequences from a fixed single camera. The System adopts TMS320DM648 as the core processor to implement the real-time target tracking algorithms, mainly complete the effective information real-time display of the software and hardware design of target tracking system, application flexibility, small volume, stable and reliable, it is very practical in practice.

scholarly journals Smart Security System Using Image Recognition

2021 ◽  
Vol 9 (VII) ◽  
pp. 1936-1939
Author(s):  
Arpita Prakash Hegde
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Good Accuracy ◽  
Family Members ◽  
Security System ◽  
Home Based ◽  
The Family

The Smart Security System using Image Recognition uses Deep Learning and Computer Vision approach.In real time it would help the home based security system to track the persons coming into the house and unlocking the door, hereby the system would be accessed by using the image recognition service in which the images are trained in different classes labeled with the names of the family members and not only them they can train the images of their relatives which provides the access to unlock their door. By using this model one can secure the home premises from the invaders and also capture the suspected people who are not authorized to move inside the house. By using “dlib one short learning”, all the faces for permission would be trained and the model is given to the security system where it can secure the premises with good accuracy through trained images.

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