scholarly journals Swimmer’s Head Detection Based on a Contrario and Scaled Composite JTC Approaches

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
D. Benarab ◽  
T. Napoléon ◽  
A. Alfalou ◽  
A. Verney ◽  
P. Hellard
Keyword(s):  
Moving Objects ◽  
Tracking System ◽  
Random Noise ◽  
Region Of Interest ◽  
Reference Image ◽  
Correlation Technique ◽  
Head Detection ◽  
Linear Transform ◽  
High Level ◽  
Direct Linear Transform

In order to accompany the swimming coaches in evaluating high-level swimmers, we developed a prototype for instantaneous speed estimation. To achieve this, we proposed and validated, in a previous work, a swimmer tracking system based on data fusion. However, the initialization phase is done manually, and our aim, in this paper, is to automate this process. First, we propose a region of interest localization module that allows the detection of the first appearance of the swimmer in the lane as well as the restriction of the region of interest around him. This module is based on the method a contrario which consists of modeling the random noise corresponding to the water and detecting the structured movement relative to the swimmer motion. To do that, we calibrate the pool using DLT (Direct Linear Transform) technique, extract the concerned lane, apply the frame difference approach to detect the moving objects, and then decompose the lane into blocs and classify them into swimmer motion or noise. Second, in order to detect the swimmer’s head, we propose the Scaled Composite JTC which is based on the NL-JTC correlation technique. The input plane of this latter includes a target and a reference image. The first is the region of interest detected by the method a contrario. The second consists of a Scaled Composite Reference. The tests conducted on real video sequences of French swimming championships (Limoges 2015) showed very good results in terms of region of interest localization and swimmer’s head detection which allows a reliable initialization for the tracking system.

GPS and GSM Based Tracking System for Objects Moving over Wide Geographical Areas

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Fatima Ameen ◽  
Ziad Mohammed ◽  
Abdulrahman Siddiq
Keyword(s):  
Global Positioning System ◽  
Mobile Communications ◽  
Moving Objects ◽  
Tracking System ◽  
Geographical Area ◽  
Tracking Systems ◽  
Acceptable Result ◽  
Internet Service ◽  
Global Positioning ◽  
Tracking Objects

Tracking systems of moving objects provide a useful means to better control, manage and secure them. Tracking systems are used in different scales of applications such as indoors, outdoors and even used to track vehicles, ships and air planes moving over the globe. This paper presents the design and implementation of a system for tracking objects moving over a wide geographical area. The system depends on the Global Positioning System (GPS) and Global System for Mobile Communications (GSM) technologies without requiring the Internet service. The implemented system uses the freely available GPS service to determine the position of the moving objects. The tests of the implemented system in different regions and conditions show that the maximum uncertainty in the obtained positions is a circle with radius of about 16 m, which is an acceptable result for tracking the movement of objects in wide and open environments.

scholarly journals Design and Experimental Characterization of a Discovery and Tracking System for Optical Camera Communications

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2925
Author(s):  
Antonio Mederos-Barrera ◽  
Cristo Jurado-Verdu ◽  
Victor Guerra ◽  
Jose Rabadan ◽  
Rafael Perez-Jimenez
Keyword(s):  
Tracking System ◽  
Region Of Interest ◽  
Experimental Methodology ◽  
Visible Light Communications ◽  
Spatial Features ◽  
Tracking Algorithms ◽  
Receiver Architecture ◽  
Vehicle Networks ◽  
Time Required ◽  
Static Conditions

Visible light communications (VLC) technology is emerging as a candidate to meet the demand for interconnected devices’ communications. However, the costs of incorporating specific hardware into end-user devices slow down its market entry. Optical camera communication (OCC) technology paves the way by reusing cameras as receivers. These systems have generally been evaluated under static conditions, in which transmitting sources are recognized using computationally expensive discovery algorithms. In vehicle-to-vehicle networks and wearable devices, tracking algorithms, as proposed in this work, allow one to reduce the time required to locate a moving source and hence the latency of these systems, increasing the data rate by up to 2100%. The proposed receiver architecture combines discovery and tracking algorithms that analyze spatial features of a custom RGB LED transmitter matrix, highlighted in the scene by varying the cameras’ exposure time. By using an anchor LED and changing the intensity of the green LED, the receiver can track the light source with a slow temporal deterioration. Moreover, data bits sent over the red and blue channels do not significantly affect detection, hence transmission occurs uninterrupted. Finally, a novel experimental methodology to evaluate the evolution of the detection’s performance is proposed. With the analysis of the mean and standard deviation of novel K parameters, it is possible to evaluate the detected region-of-interest scale and centrality against the transmitter source’s ideal location.

The Research of Moving Target Detection and Tracking System Based on DM6437

2015 ◽  
Vol 734 ◽  
pp. 203-206
Author(s):  
En Zeng Dong ◽  
Sheng Xu Yan ◽  
Kui Xiang Wei
Keyword(s):  
Target Detection ◽  
Digital Signal Processor ◽  
Moving Objects ◽  
Tracking System ◽  
Digital Signal ◽  
Gaussian Mixture ◽  
Background Model ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection And Tracking

In order to enhance the rapidity and the accuracy of moving target detection and tracking, and improve the speed of the algorithm on the DSP (digital signal processor), an active visual tracking system was designed based on the gaussian mixture background model and Meanshift algorithm on DM6437. The system use the VLIB library developed by TI, and through the method of gaussian mixture background model to detect the moving objects and use the Meanshift tracking algorithm based on color features to track the target in RGB space. Finally, the system is tested on the hardware platform, and the system is verified to be quickness and accuracy.

Image processed tracking system of multiple moving objects based on Kalman filter

2002 ◽  
Vol 16 (4) ◽  
pp. 427-435
Author(s):  
Dong-Kyu Kim ◽  
Sang-Bong Kim ◽  
Hak-Kyeong Kim
Keyword(s):  
Kalman Filter ◽  
Moving Objects ◽  
Tracking System

scholarly journals Dynamic Autonomous Identification and Intelligent Lighting of Moving Objects with Discomfort Glare Limitation

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7243
Author(s):  
Sebastian Słomiński ◽  
Magdalena Sobaszek
Keyword(s):  
Moving Objects ◽  
Tracking System ◽  
Electricity Consumption ◽  
Computation Time ◽  
Projection Image ◽  
Reduce Electricity Consumption ◽  
Smart Control ◽  
Discomfort Glare ◽  
Lighting Systems ◽  
Dynamic Lighting

The importance of reducing discomfort glare during the dynamic development of high luminance LEDs is growing fast. Smart control systems also offer great opportunities to reduce electricity consumption for lighting purposes. Currently, dynamic “intelligent” lighting systems are a rapidly developing field. These systems, consisting of cameras and lighting units, such as moving heads or multimedia projectors, are powerful tools that provide a lot of opportunities. The aim of this research is to demonstrate the possibilities of using the projection light in dynamic lighting systems that enable the reduction of discomfort glare and the light pollution phenomenon. The proposed system allows darkening or reducing the luminance of some sensitive zones, such as the eyes or the head, in real-time. This paper explores the development of the markerless object tracking system. The precise identification of the position and geometry of objects and the human figure is used for dynamic lighting and mapping with any graphic content. Time measurements for downloading the depth maps, as well as for identifying the human body’s position and pose, have been performed. The analyses of the image transformation times have been carried out in relation to the resolution of the images displayed by the projector. The total computation time related to object detection and image display translates directly into the precision of fitting the projection image to a moving object and has been shown.

Feature-Based Affine Motion Estimation for Superresolution of a Region of Interest

2013 ◽  
pp. 118-137
Author(s):  
Sung Hyun Kim ◽  
Rae-Hong Park ◽  
Seungjoon Yang ◽  
Hwa-Young Kim
Keyword(s):  
Motion Estimation ◽  
Convex Sets ◽  
Interpolation Method ◽  
Digital Camera ◽  
Region Of Interest ◽  
Noise Model ◽  
Reference Image ◽  
Multiple Images ◽  
Affine Motion ◽  
Feature Based

This chapter presents an interpolation method of low-computation for a Region Of Interest (ROI) using multiple low-resolution images of the same scene. Interpolation methods using multiple images require the accurate motion information between the reference image of interpolation and the other images. Sometimes complex local motions applied to the entire images are estimated incorrectly, yielding seriously degraded interpolation results. The authors apply the proposed Superresolution (SR) method, which employs a simple global motion model, only to the ROI that contains important information of the scene. The ROIs extracted from multiple images are assumed to have simple global motions. At first, using a mean absolute difference measure, they extract the regions from the multiple images that are similar to the selected ROI in the reference image of interpolation and use feature points to estimate the affine motion parameters. The authors apply the Projection Onto Convex Sets (POCS)-based method to the ROI using the estimated motion, simplify the iterative computation of the whole system, and use an edge-preserving smoothing filter to reduce the distortion caused by additive noise. In experiments, they acquire test image sets with a hand-held digital camera and use a Gaussian noise model. Experimental results show that the feature-based Motion Estimation (ME) is accurate and reducing the computational load of the ME step is efficient in terms of the computational complexity. It is also shown that the SR results using the proposed method are remarkable even when input images contain complex motions and a large amount of noise. The proposed POCS-based SR algorithm can be applied to digital cameras, portable camcorders, and so on.

scholarly journals Suboptimal Engagement of High-Level Cortical Regions Predicts Random-Noise-Related Gains in Sustained Attention

2019 ◽  
Vol 30 (9) ◽  
pp. 1318-1332 ◽  
Author(s):  
Siobhán Harty ◽  
Roi Cohen Kadosh
Keyword(s):  
Sustained Attention ◽  
Random Noise ◽  
Explanatory Power ◽  
Psychological Research ◽  
Cognitive Outcomes ◽  
Double Blind ◽  
Attention Task ◽  
Transcranial Random Noise Stimulation ◽  
Cortical Regions ◽  
High Level

Interindividual variability in outcomes across individuals poses great challenges for the application of noninvasive brain stimulation in psychological research. Here, we examined how the effects of high-frequency transcranial random-noise stimulation (tRNS) on sustained attention varied as a function of a well-studied electrocortical marker: spontaneous theta:beta ratio. Seventy-two participants received sham, 1-mA, and 2-mA tRNS in a double-blind, crossover manner while they performed a sustained-attention task. Receiving 1-mA tRNS was associated with improved sustained attention, whereas the effect of 2-mA tRNS was similar to the effect of sham tRNS. Furthermore, individuals’ baseline theta:beta ratio moderated the effects of 1-mA tRNS and provided explanatory power beyond baseline behavioral performance. The tRNS-related effects on sustained attention were also accompanied by reductions in theta:beta ratio. These findings impart novel insights into mechanisms underlying tRNS effects and emphasize how designing studies that link variability in cognitive outcomes to variability in neurophysiology can improve inferential power in neurocognitive research.

On the Problem of Moving Objects With Autonomous Robots: A Unifying High-Level Planning Approach

2016 ◽  
Vol 1 (1) ◽  
pp. 469-476 ◽  
Author(s):  
Hamal Marino ◽  
Mirko Ferrati ◽  
Alessandro Settimi ◽  
Carlos Rosales ◽  
Marco Gabiccini
Keyword(s):  
Moving Objects ◽  
Autonomous Robots ◽  
Planning Approach ◽  
High Level

MOTION DETECTION FROM TIME-VARIED BACKGROUND

2002 ◽  
Vol 02 (02) ◽  
pp. 163-178 ◽  
Author(s):  
YING REN ◽  
CHIN SENG CHUA ◽  
YEONG KHING HO
Keyword(s):  
Motion Compensation ◽  
Background Subtraction ◽  
Moving Objects ◽  
Environmental Changes ◽  
Tracking System ◽  
Extended Period ◽  
Test Cases ◽  
Adaptation Algorithm ◽  
Current Frame ◽  
Varied Background

This paper proposes a new background subtraction method for detecting moving objects (foreground) from a time-varied background. While background subtraction has traditionally worked well for stationary backgrounds, for a non-stationary viewing sensor, motion compensation can be applied but is difficult to realize to sufficient pixel accuracy in practice, and the traditional background subtraction algorithm fails. The problem is further compounded when the moving target to be detected/tracked is small, since the pixel error in motion compensating the background will subsume the small target. A Spatial Distribution of Gaussians (SDG) model is proposed to deal with moving object detection under motion compensation that has been approximately carried out. The distribution of each background pixel is temporally and spatially modeled. Based on this statistical model, a pixel in the current frame is classified as belonging to the foreground or background. For this system to perform under lighting and environmental changes over an extended period of time, the background distribution must be updated with each incoming frame. A new background restoration and adaptation algorithm is developed for the time-varied background. Test cases involving the detection of small moving objects within a highly textured background and a pan-tilt tracking system based on a 2D background mosaic are demonstrated successfully.

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