Computer Vision Based Analysis for Cursor Control Using Object Tracking and Color Detection

2014 ◽  
Author(s):  
Dion Firmanda ◽  
Dadet Pramadihanto
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Cursor Control ◽  
Color Detection

scholarly journals An Efficient and Robust Multi-Object Recognition and Tracking Algorithm using Mask Region based Convolution Neural Network (R-CNN)

2019 ◽  
Vol 8 (9) ◽  
pp. 607-613
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Object Recognition ◽  
Object Tracking ◽  
Ground Truth ◽  
Benchmark Dataset ◽  
Tracking Algorithm ◽  
Multiple Objects ◽  
High Quality ◽  
Extensive Simulation

Presently, Multi-Object tracking (MOT) is mainly applied for predicting the positions of many predefined objects across many successive frames with the provided ground truth position of the target in the first frame. The area of MOT gains more interest in the area of computer vision because of its applicability in various fields. Many works have been presented in recent years that intended to design a MOT algorithm with maximum accuracy and robustness. In this paper, we introduce an efficient as well as robust MOT algorithm using Mask R-CNN. The usage of Mask R-CNN effectively identifies the objects present in the image while concurrently creating a high-quality segmentation mask for every instance. The presented MOT algorithm is validated using three benchmark dataset and the results are extensive simulation. The presented tracking algorithm shows its efficiency to track multiple objects precisely

scholarly journals Fast and Economical Object Tracking using Raspberry Pi 3.0

2019 ◽  
Vol 8 (6S) ◽  
pp. 336-338
Keyword(s):  
Computer Vision ◽  
Power Consumption ◽  
Low Power ◽  
Object Detection ◽  
Object Tracking ◽  
Tracking System ◽  
Low Power Consumption ◽  
Raspberry Pi ◽  
Multiple Object Detection ◽  
Vision Application

This paper proposes a way to construct a financially cheap and fast object tracking using Raspberry Pi3. Multiple object detection is an important step in any computer vision application. Since the number of cameras included is more these gadgets are compelled by expense per hub, control utilization and handling power. We propose a tracking system with low power consumption. The framework is completely designed with python and OpenCV. The tracking quality and accuracy is measured using publicly available datasets.

scholarly journals Appearance modeling for persistent object tracking in wide-area and full motion video

2016 ◽  
Author(s):  
◽  
Rengarajan Pelapur
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Target Tracking ◽  
Computational Cost ◽  
Wide Area ◽  
Appearance Model ◽  
Urban Scenes ◽  
Update Strategy ◽  
Track Before Detect ◽  
Motion Video

Object tracking is a core element of computer vision and autonomous systems. As such single and multiple object tracking has been widely investigated especially for full motion video sequences. The acquisition of wide-area motion imagery (WAMI) from moving airborne platforms is a much more recent sensor innovation that has an array of defense and civilian applications with numerous opportunities for providing a unique combination of dense spatial and temporal coverage unmatched by other sensor systems. Airborne WAMI presents a host of challenges for object tracking including large data volume, multi-camera arrays, image stabilization, low resolution targets, target appearance variability and high background clutter especially in urban environments. Time varying low frame rate large imagery poses a range of difficulties in terms of reliable long term multi-target tracking. The focus of this thesis is on the Likelihood of Features Tracking (LOFT) testbed system that is an appearance based (single instance) object tracker designed specifcally for WAMI and follows the track before detect paradigm. The motivation for tracking using dynamics before detecting was so that large scale data can be handled in an environment where computational cost can be kept at a bare minimum. Searching for an object everywhere on a large frame is not practical as there are many similar objects, clutter, high rise structures in case of urban scenes and comes with the additional burden of greatly increased computational cost. LOFT bypasses this difficulty by using filtering and dynamics to constrain the search area to a more realistic region within the large frame and uses multiple features to discern objects of interest. The objects of interest are expected as input in the form of bounding boxes to the algorithm. The main goal of this work is to present an appearance update modeling strategy that fits LOFT's track before detect paradigm and to showcase the accuracy of the overall system as compared with other state of the art tracking algorithms and also with and without the presence of this strategy. The update strategy using various information cues from the Radon Transform was designed with certain performance parameters in mind such as minimal increase in computational cost and a considerable increase in precision and recall rates of the overall system. This has been demonstrated with supporting performance numbers using standard evaluation techniques as in literature. The extensions of LOFT WAMI tracker to include a more detailed appearance model with an update strategy that is well suited for persistent target tracking is novel in the opinion of the author. Key engineering contributions have been made with the help of this work wherein the core LOFT has been evaluated as part several government research and development programs including the Air Force Research Lab's Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) Enterprise to the Edge (CETE), Army Research Lab's Advanced Video Activity Analytics (AVAA) and a proposed fine grained distributed computing architecture on the cloud for processing at the edge. A simplified version of LOFT was developed for tracking objects in standard videos and entered in the Visual Object Tracking (VOT) Challenge competition that is held in conjunction with the leading computer vision conferences. LOFT incorporating the proposed appearance adaptation module produces significantly better tracking results in aerial WAMI of urban scenes.

scholarly journals How to Generate Image Dataset based on 3D Model and Deep Learning Method

2018 ◽  
Vol 7 (3.34) ◽  
pp. 221
Author(s):  
Sooyoung Cho ◽  
Sang Geun Choi ◽  
Daeyeol Kim ◽  
Gyunghak Lee ◽  
Chae BongSohn
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Recognition ◽  
Object Tracking ◽  
3D Model ◽  
Object Segmentation ◽  
Learning Method ◽  
Data Set ◽  
Image Dataset ◽  
Deep Learning Model

Performances of computer vision tasks have been drastically improved after applying deep learning. Such object recognition, object segmentation, object tracking, and others have been approached to the super-human level. Most of the algorithms were trained by using supervised learning. In general, the performance of computer vision is improved by increasing the size of the data. The collected data was labeled and used as a data set of the YOLO algorithm. In this paper, we propose a data set generation method using Unity which is one of the 3D engines. The proposed method makes it easy to obtain the data necessary for learning. We classify 2D polymorphic objects and test them against various data using a deep learning model. In the classification using CNN and VGG-16, 90% accuracy was achieved. And we used Tiny-YOLO of YOLO algorithm for object recognition and we achieved 78% accuracy. Finally, we compared in terms of virtual and real environments it showed a result of 97 to 99 percent for each accuracy.

scholarly journals A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2641 ◽  
Author(s):  
Anca Morar ◽  
Alin Moldoveanu ◽  
Irina Mocanu ◽  
Florica Moldoveanu ◽  
Ion Emilian Radoi ◽  
...  
Keyword(s):  
Computer Vision ◽  
Augmented Reality ◽  
Object Tracking ◽  
Indoor Localization ◽  
New Classification ◽  
Localization Method ◽  
Localization Domain ◽  
Mobile Cameras ◽  
Comprehensive Survey ◽  
Orientation Information

Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with images acquired during a configuration stage or extracted from 3D reconstructed models of the space. This paper offers an overview of the computer vision based indoor localization domain, presenting application areas, commercial tools, existing benchmarks, and other reviews. It provides a survey of indoor localization research solutions, proposing a new classification based on the configuration stage (use of known environment data), sensing devices, type of detected elements, and localization method. It groups 70 of the most recent and relevant image based indoor localization methods according to the proposed classification and discusses their advantages and drawbacks. It highlights localization methods that also offer orientation information, as this is required by an increasing number of applications of indoor localization (e.g., augmented reality).

Multi-Object Tracking with Single Camera

2015 ◽  
Vol 740 ◽  
pp. 668-671
Author(s):  
Yu Bing Dong ◽  
Ying Sun ◽  
Ming Jing Li
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Block Matching ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Single Camera ◽  
Trajectory Prediction ◽  
Tracking Method

Multi-object tracking has been a challenging topic in computer vision. A Simple and efficient moving multi-object tracking algorithm is proposed. A new tracking method combined with trajectory prediction and a sub-block matching is used to handle the objects occlusion. The experimental results show that the proposed algorithm has good performance.

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