scholarly journals Object Tracking Using HSV Values and OpenCV

2021 ◽  
Vol 9 (12) ◽  
pp. 594-599
Author(s):  
Gowher Shafi
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Moving Objects ◽  
Video Tracking ◽  
Frame Rate ◽  
Colour Space ◽  
Long Distance ◽  
Video Frames ◽  
Hsv Colour Space ◽  
Percent Accuracy

Abstract: This research shows how to use colour and movement to automate the process of recognising and tracking things. Video tracking is a technique for detecting a moving object over a long distance using a camera. The main purpose of video tracking is to connect target objects in subsequent video frames. The connection may be particularly troublesome when things move faster than the frame rate. Using HSV colour space values and OpenCV in different video frames, this study proposes a way to track moving objects in real-time. We begin by calculating the HSV value of an item to be monitored, and then we track the object throughout the testing step. The items were shown to be tracked with 90 percent accuracy. Keywords: HSV, OpenCV, Object tracking, Video frames, GUI

scholarly journals An Efficient Real Time Object Tracking Based on HSV Value

2021 ◽  
Vol 9 (11) ◽  
pp. 1197-1203
Author(s):  
Sheikh Summerah
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Moving Objects ◽  
Color Space ◽  
Video Tracking ◽  
Frame Rate ◽  
Long Distance ◽  
Hsv Color Space ◽  
Video Frames ◽  
Testing Stage

Abstract: This study presents a strategy to automate the process to recognize and track objects using color and motion. Video Tracking is the approach to detect a moving item using a camera across the long distance. The basic goal of video tracking is in successive video frames to link target objects. When objects move quicker in proportion to frame rate, the connection might be particularly difficult. This work develops a method to follow moving objects in real-time utilizing HSV color space values and OpenCV in distinct video frames.. We start by deriving the HSV value of an object to be tracked and then in the testing stage, track the object. It was seen that the objects were tracked with 90% accuracy. Keywords: HSV, OpenCV, Object tracking,

Video Tracking of Human with Occlusion Based on MeanShift and Kalman Filter

2013 ◽  
Vol 380-384 ◽  
pp. 3672-3677 ◽  
Author(s):  
Bao Hong Yuan ◽  
De Xiang Zhang ◽  
Kui Fu ◽  
Ling Jun Zhang
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Moving Objects ◽  
Video Tracking ◽  
Moving Object ◽  
Large Area ◽  
Image Region ◽  
Future State ◽  
Kalman Filter Algorithm ◽  
Histogram Similarity

In order to accomplish tracking of moving objects requirements, and overcome the defect of occlusion in the process of tracking moving object, this paper presents a method which uses a combination of MeanShift and Kalman filter algorithm. MeanShift object tracking algorithm uses a histogram to describe the color characteristics of an object, and search the location of an image region that the color histogram is closest to the histogram of the object. Histogram similarity is defined in terms of the Bhattacharya coefficient. When the moving object is a large area blocked, the future state of moving object is estimated by Kalman filter. Experimental results verify that the proposed algorithm achieves efficient tracking of moving objects under the confusing situations.

scholarly journals Asynchronous Semantic Background Subtraction

2020 ◽  
Vol 6 (6) ◽  
pp. 50
Author(s):  
Anthony Cioppa ◽  
Marc Braham ◽  
Marc Van Droogenbroeck
Keyword(s):  
Real Time ◽  
Background Subtraction ◽  
Semantic Information ◽  
Moving Objects ◽  
Temporal Evolution ◽  
Semantic Segmentation ◽  
Feedback Mechanism ◽  
Segmentation Algorithm ◽  
Frame Rate ◽  
The One

The method of Semantic Background Subtraction (SBS), which combines semantic segmentation and background subtraction, has recently emerged for the task of segmenting moving objects in video sequences. While SBS has been shown to improve background subtraction, a major difficulty is that it combines two streams generated at different frame rates. This results in SBS operating at the slowest frame rate of the two streams, usually being the one of the semantic segmentation algorithm. We present a method, referred to as “Asynchronous Semantic Background Subtraction” (ASBS), able to combine a semantic segmentation algorithm with any background subtraction algorithm asynchronously. It achieves performances close to that of SBS while operating at the fastest possible frame rate, being the one of the background subtraction algorithm. Our method consists in analyzing the temporal evolution of pixel features to possibly replicate the decisions previously enforced by semantics when no semantic information is computed. We showcase ASBS with several background subtraction algorithms and also add a feedback mechanism that feeds the background model of the background subtraction algorithm to upgrade its updating strategy and, consequently, enhance the decision. Experiments show that we systematically improve the performance, even when the semantic stream has a much slower frame rate than the frame rate of the background subtraction algorithm. In addition, we establish that, with the help of ASBS, a real-time background subtraction algorithm, such as ViBe, stays real time and competes with some of the best non-real-time unsupervised background subtraction algorithms such as SuBSENSE.

Optimizing Design of Real-Time Spatial Moving Target Tracking System

2011 ◽  
Vol 328-330 ◽  
pp. 2234-2237
Author(s):  
Dong Sheng Liang ◽  
Zhao Hui Liu ◽  
Wen Liu
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Tracking System ◽  
Processing System ◽  
Video Tracking ◽  
Image Resolution ◽  
Frame Rate ◽  
Moving Target ◽  
Detection And Tracking ◽  
Operation Speed

Achieving the detection and tracking of moving targets has been widely applied in all fields of today's society. Because of the shortcomings of traditional video tracking system, this paper proposes a novel method for designing video processing system based on hardware design of FPGA and DSP, and moving target in video can be detected and tracked by this system. In this system, DSP as the core of the system, it mainly completes the processing algorithms of video and image data, FPGA as a coprocessor, responsible for the completion of the processing of external data and logic. The hardware structure, link configuration, program code and other aspects of system are optimized. Finally, through the experiment, the input frame rate of video is 40frames/s, and the image resolution is 512pixels × 512pixels, median 16bites quantitative image sequence, the system can complete the relevant real-time detection and tracking algorithm and extract targets position of image sequences correctly. The results show that the advantage is that this system has powerful operation speed, real time, high accuracy and stability.

scholarly journals Real-time markerless video tracking of body parts in mice using deep neural networks

2018 ◽  
Author(s):  
Brandon Forys ◽  
Dongsheng Xiao ◽  
Pankaj Gupta ◽  
Jamie D Boyd ◽  
Timothy H Murphy
Keyword(s):  
Real Time ◽  
Behavioral Science ◽  
Behavioral Control ◽  
Brain Regions ◽  
Video Tracking ◽  
Frame Rate ◽  
Body Parts ◽  
Neural Basis ◽  
Movement Initiation ◽  
Optogenetic Stimulation

ABSTRACTMarkerless and accurate tracking of mouse movement is of interest to many biomedical, pharmaceutical, and behavioral science applications. The additional capability of tracking body parts in real-time with minimal latency opens up the possibility of manipulating motor feedback, allowing detailed explorations of the neural basis for behavioral control. Here we describe a system capable of tracking specific movements in mice at a frame rate of 30.3 Hz. To achieve these results, we adapt DeepLabCut – a robust movement-tracking deep neural network framework – for real-time tracking of body movements in mice. We estimate paw movements of mice in real time and demonstrate the concept of movement-triggered optogenetic stimulation by flashing a USB-CGPIO controlled LED that is triggered when real time analysis of movement exceeds a pre-set threshold. The mean time delay between movement initiation and LED flash was 93.44 ms, a latency sufficient for applying behaviorally-triggered feedback. This manuscript presents the rationale and details of the algorithms employed and shows implementation of the system using behaving mice. This system lays the groundwork for a behavior-triggered ‘closed loop’ brain-machine interface with optogenetic stimulation of specific brain regions for feedback.

scholarly journals Object Tracking in Hyperspectral-Oriented Video with Fast Spatial-Spectral Features

2021 ◽  
Vol 13 (10) ◽  
pp. 1922
Author(s):  
Lulu Chen ◽  
Yongqiang Zhao ◽  
Jiaxin Yao ◽  
Jiaxin Chen ◽  
Ning Li ◽  
...  
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Spatial Information ◽  
Video Tracking ◽  
Spectral Features ◽  
Surveillance Video ◽  
Tracking Accuracy ◽  
Low Resolution ◽  
Background Clutter ◽  
Appearance Changes

This paper presents a correlation filter object tracker based on fast spatial-spectral features (FSSF) to realize robust, real-time object tracking in hyperspectral surveillance video. Traditional object tracking in surveillance video based only on appearance information often fails in the presence of background clutter, low resolution, and appearance changes. Hyperspectral imaging uses unique spectral properties as well as spatial information to improve tracking accuracy in such challenging environments. However, the high-dimensionality of hyperspectral images causes high computational costs and difficulties for discriminative feature extraction. In FSSF, the real-time spatial-spectral convolution (RSSC) kernel is updated in real time in the Fourier transform domain without offline training to quickly extract discriminative spatial-spectral features. The spatial-spectral features are integrated into correlation filters to complete the hyperspectral tracking. To validate the proposed scheme, we collected a hyperspectral surveillance video (HSSV) dataset consisting of 70 sequences in 25 bands. Extensive experiments confirm the advantages and the efficiency of the proposed FSSF for object tracking in hyperspectral video tracking in challenging conditions of background clutter, low resolution, and appearance changes.

scholarly journals Visual-Feedback-Based Frame-by-Frame Synchronization for 3000 fps Projector–Camera Visual Light Communication

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1631
Author(s):  
Atul Sharma ◽  
Sushil Raut ◽  
Kohei Shimasaki ◽  
Taku Senoo ◽  
Idaku Ishii
Keyword(s):  
Real Time ◽  
Visual Feedback ◽  
Video Sequence ◽  
Clock Cycle ◽  
Frame Rate ◽  
Video Sequences ◽  
Frame Synchronization ◽  
Bandwidth Utilization ◽  
Long Distance ◽  
Synchronization Algorithm

This paper proposes a novel method for synchronizing a high frame-rate (HFR) camera with an HFR projector, using a visual feedback-based synchronization algorithm for streaming video sequences in real time on a visible-light communication (VLC)-based system. The frame rates of the camera and projector are equal, and their phases are synchronized. A visual feedback-based synchronization algorithm is used to mitigate the complexities and stabilization issues of wire-based triggering for long-distance systems. The HFR projector projects a binary pattern modulated at 3000 fps. The HFR camera system operates at 3000 fps, which can capture and generate a delay signal to be given to the next camera clock cycle so that it matches the phase of the HFR projector. To test the synchronization performance, we used an HFR projector–camera-based VLC system in which the proposed synchronization algorithm provides maximum bandwidth utilization for the high-throughput transmission ability of the system and reduces data redundancy efficiently. The transmitter of the VLC system encodes the input video sequence into gray code, which is projected via high-definition multimedia interface streaming in the form of binary images 590 × 1060. At the receiver, a monochrome HFR camera can simultaneously capture and decode 12-bit 512 × 512 images in real time and reconstruct a color video sequence at 60 fps. The efficiency of the visual feedback-based synchronization algorithm is evaluated by streaming offline and live video sequences, using a VLC system with single and dual projectors, providing a multiple-projector-based system. The results show that the 3000 fps camera was successfully synchronized with a 3000 fps single-projector and a 1500 fps dual-projector system. It was confirmed that the synchronization algorithm can also be applied to VLC systems, autonomous vehicles, and surveillance applications.

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