scholarly journals Real Time Tracking RGB Color Based Kinect

2017 ◽  
Vol 11 (3) ◽  
pp. 98
Author(s):  
Ahmed Mustafa Taha Alzbier ◽  
Hang Cheng
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
Video Processing ◽  
Background Subtraction ◽  
Moving Objects ◽  
Blue Color ◽  
The Real ◽  
Color Recognition ◽  
Kinect Camera ◽  
Computer Vision Technology

As the present computer vision technology is growing up, and the multiple RGB color object tracking is considered as one of the important tasks in computer vision and technique that can be used in many applications such as surveillance in a factory production line, event organization, flow control application, analysis and sort by colors and etc. In video processing applications, variants of the background subtraction method are broadly used for the detection of moving objects in video sequences. The background subtraction is the most popular and common approach for motion detection. However , this is paper presents our investigation the first objective of the whole algorithm chain is to find the RGB color within a video. The idea from the beginning was to look for certain specific features of the patches, which would allow distinguishing red, green and blue color objects in the image. In this paper an algorithm is proposed to track the real time moving RGB color objects using kinect camera. We will use a kinect camera to capture the real time video and making an image frame from this video and extracting red, green and blue color .Here image processing is done through MATLAB for color recognition process each color. Our method can tracking accurately at 95% in real-time.

Multi Object Detection and Tracking from Video File

2014 ◽  
Vol 533 ◽  
pp. 218-225 ◽  
Author(s):  
Rapee Krerngkamjornkit ◽  
Milan Simic
Keyword(s):  
Computer Vision ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Video Tracking ◽  
Partial Occlusion ◽  
Video File ◽  
Detection And Tracking ◽  
Over Time

This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results show that system can operate in real time and successfully detect, track and identify multiple targets in the presence of partial occlusion.

Algorithm of Color Detection for Moving Video Objects Based on Mode Matching

2012 ◽  
Vol 239-240 ◽  
pp. 1000-1003
Author(s):  
Zhao Quan Cai ◽  
Hui Hu ◽  
Tao Xu ◽  
Wei Luo ◽  
Yi Cheng He
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Color Matching ◽  
Human Beings ◽  
Current Image ◽  
Color Recognition ◽  
Recognition Ability ◽  
Color Identification ◽  
The Difference ◽  
Background Image

It is urgent to study how to effectively identify color of moving objects from the video in the information era. In this paper, we present the color identification methods for moving objects on fixed camera. One kind of the methods is background subtraction that recognizes the foreground objects by compare the difference of pixel luminance between the current image and the background image at the same coordinates. Another kind is based on the statistics of HSV color and color matching which makes the detection more similar to the color identification of the human beings. According to the experiment results, after the completion of the background modelling, our algorithm of background subtraction, statistics of the HSV color and the color matching have strong color recognition ability on the moving objects of video.

Digital Image Zero-Watermarking Algorithm Based on Improved Wavelet Moment

2013 ◽  
Vol 347-350 ◽  
pp. 3232-3236
Author(s):  
Zheng Bao Zhang ◽  
Chao Jia
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Real Time ◽  
Digital Watermarking ◽  
Digital Image ◽  
Image Rotation ◽  
The Real ◽  
Geometric Attacks ◽  
Zero Watermarking ◽  
Local Distortions

Lots of anti-RST attacks watermarking algorithms have been proposed, but few solutions for local geometric attacks, in this paper it proposed a new algorithm combined with the the Wavelet Moment for an anti-geometric attacks. Since wavelet moment was proposed, it is widely used in the field of computer vision, image processing, but the large amount of computation must be improved to be applied to digital watermarking technology so that it can adapt to the real-time detection of digital watermarking. By image rotation, scaling, translation, shear, local distortions, filtering attack operations and so on, these attacks can be seen that the algorithm has good robustness, and the efficiency of watermark detection is relatively high. The experiments show that the algorithm is robustness, greatly accelerate the speed of operation, to unify the robust and efficient.

Distributed Video Coding and Content Analysis for Resource Constraint Multimedia Applications

2012 ◽  
pp. 251-273
Author(s):  
Praveen Kumar ◽  
Amit Pande ◽  
Ankush Mittal ◽  
Abhisek Mudgal
Keyword(s):  
Video Coding ◽  
Real Time ◽  
Video Processing ◽  
Mobile Agents ◽  
Video Transmission ◽  
Moving Objects ◽  
Individual Case ◽  
Multimedia Applications ◽  
Real Time Processing ◽  
Laboratory Setup

Video coding and analysis for low power and low bandwidth multimedia applications has always been a great challenge. The limited computational resources on ubiquitous multimedia devices like cameras along with low and varying bandwidth over wireless network lead to serious bottlenecks in delivering real-time streaming of videos for such applications. This work presents a Content-based Network-adaptive Video-transmission (CbNaVt) framework which can waive off the requirements of low bandwidth. This is done by transmitting important content only to the end user. The framework is illustrated with the example of video streaming in the context of remote laboratory setup. A framework for distributed processing using mobile agents is discussed with the example of Distributed Video Surveillance (DVS). In this regard, the increased computational costs due to video processing tasks like object segmentation and tracking are shared by the cameras and a local base station called as Processing Proxy Server (PPS).However, in a distributed scenario like traffic surveillance, where moving objects is tracked using multiple cameras, the processing tasks needs to be dynamically distributed. This is done intelligently using mobile agents by migrating from one PPS to another for tracking an individual case object and transmitting required information to the end users. Although the authors propose a specific implementation for CbNaVt and DVS systems, the general ideas in design of such systems exemplify the way information can be intelligently transmitted in any ubiquitous multimedia applications along with the use of mobile agents for real-time processing and retrieval of video signal.

Computer vision technology for real-time food quality assurance during drying process

2014 ◽  
Vol 39 (1) ◽  
pp. 76-84 ◽  
Author(s):  
Mortaza Aghbashlo ◽  
Soleiman Hosseinpour ◽  
Mahdi Ghasemi-Varnamkhasti
Keyword(s):  
Computer Vision ◽  
Quality Assurance ◽  
Real Time ◽  
Food Quality ◽  
Drying Process ◽  
Computer Vision Technology

scholarly journals A Computer Vision-Based Real-Time Load Perception Method for Belt Conveyors

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Mengchao Zhang ◽  
Manshan Zhou ◽  
Hao Shi
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
Conveyor Belt ◽  
Conveyor System ◽  
Load Detection ◽  
Speed Regulation ◽  
Computer Vision Technology ◽  
Belt Conveyors ◽  
Energy Saving Control ◽  
Load Perception

Real-time load detection method for belt conveyors based on computer vision is the research topic of this paper. A belt conveyor system equipped with cameras and a laser generator is used as the test apparatus. As the basis for conveyor intelligent speed regulation, two methods from different angles to perceive the load of conveyor belt were proposed, applied, and compared in this paper. Method 1 is based on the area proportion and method 2 is the detection based on laser-based computer vision technology. Laboratory experiments show that both methods can well detect the load on the conveyor belt. Method 2 is more economical and practical under the background of existing technology, also compared to the method 1, which provides a new idea and theoretical basis for the energy-saving control and intelligent development of the conveyor.

scholarly journals FPGA-Based Stereo-Vision System Mimicking Human Binocular Vision

2021 ◽  
Author(s):  
Gvarami Labartkava
Keyword(s):  
Real Time ◽  
Binocular Vision ◽  
Video Processing ◽  
High Performance ◽  
Moving Objects ◽  
Peripheral Vision ◽  
Vision System ◽  
Processing System ◽  
Object Motion ◽  
Human Vision

Human vision is a complex system which involves processing frames and retrieving information in a real-time with optimization of the memory, energy and computational resources usage. It can be widely utilized in many real-world applications from security systems to space missions. The research investigates fundamental principles of human vision and accordingly develops a FPGA-based video processing system with binocular vision, capable of high performance and real-time tracking of moving objects in 3D space. The undertaken research and implementation consist of: 1. Analysis of concepts and methods of human vision system; 2. Development stereo and peripheral vision prototype of a system-on-programmable chip (SoPC) for multi-object motion detection and tracking; 3. Verification, test run and analysis of the experimental results gained on the prototype and associated with the performance constraints; The implemented system proposes a platform for real-time applications which are limited in current approaches.

scholarly journals Object Detection and Tracking in Real Time Video Stream

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
N. Jayanti ◽  
Keyword(s):  
Real Time ◽  
Forest Fires ◽  
Video Processing ◽  
Fire Detection ◽  
Video Stream ◽  
Image Subtraction ◽  
Color Recognition ◽  
Detection And Tracking ◽  
Video Feed ◽  
Fully Automatic

To achieve fully automatic surveillance of some specific color objects, an intelligent real-time detection method based on video processing is proposed. The main aim of this paper is to identify the colors and use them to achieve their applications. The proposed algorithm is used to detect a specific color and also to track it in the live video feed which could be eventually used for many different applications like surveillance cameras, fire detection in cases of forest fires, etc. For the color recognition part, several stages such as image subtraction, noise filtering, binary image, and blob extraction are used to recognize a specific color in the video feed. Then the corresponding pixels on the GUI are drawn to track where all the color has been. This might find application in various areas; one such area in which this has been used often is in the detection of forest fires.

scholarly journals Background Subtraction (BS) Using Instant Pixel Histogram

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Narjis Mezaal Shati ◽  
Sundos Abdulameer Alazawi ◽  
Huda Abdulaali Abdulbaqi
Keyword(s):  
Computer Vision ◽  
False Alarm ◽  
False Alarm Rate ◽  
Background Subtraction ◽  
Detection Rate ◽  
Moving Objects ◽  
Moving Objects Detection ◽  
Computer Vision Applications ◽  
Objects Detection ◽  
Published Research

Video computer vision applications require moving objects detection as a first phase of their operation. Therefore, background subtraction (BS), an investigate branch in computer vision with intensive published research, is applied to obtain the “background” and the “foreground.” Our study proposes a new BS model that utilizes instant pixel histogram, which is implemented to extract foreground objects from two datasets, the first Visor (different human actions) and the second Anomaly Detection Dataset UCSD (Peds2). The model when using the Visor dataset gives 100% detection rate with 8% false alarm rate, whereas, when using UCSD (Peds2), it achieves a detection rate and false alarm rate of 77% and 34% respectively.

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