scholarly journals Research on the Detection and Tracking Algorithm of Moving Object in Image Based on Computer Vision Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Chunsheng Chen ◽  
Din Li
Keyword(s):  
Computer Vision ◽  
Edge Detection ◽  
Moving Object ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Running Time ◽  
Detection And Tracking ◽  
Camshift Algorithm ◽  
Computer Vision Technology ◽  
Background Difference

In order to improve the video image processing technology, this paper presents a moving object detection and tracking algorithm based on computer vision technology. Firstly, the detection performance of the interframe difference method and the background difference model method is compared comprehensively from both theoretical and experimental aspects, and then the Robert edge detection operator is selected to carry out edge detection of the vehicle. The research results show that the algorithm proposed in this paper has the longest running time per frame when tracking a moving target, which is about 2.3 times that of the single frame running time of the CamShift algorithm. The algorithm has high running efficiency and can meet the requirements of real-time tracking of a foreground target. The algorithm has the highest tracking accuracy, the time consumption is reduced, and the error of the tracking frame deviating from the real position of the target is the least.

Research on Moving Vehicle Detection and Tracking Algorithm Based on Video Image

2011 ◽  
Vol 467-469 ◽  
pp. 1488-1492 ◽  
Author(s):  
Xin Sha Fu ◽  
Juan Zhu
Keyword(s):  
Difference Method ◽  
Moving Objects ◽  
Vehicle Detection ◽  
Tracking Algorithm ◽  
Moving Vehicle ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Computer Vision Technology ◽  
Background Difference ◽  
Moving Vehicle Detection

Based on the computer vision technology and the digital image processing technology, the video moving vehicle detection and tracking algorithm is made to be on research; with the base of each characters of the background difference method and the inter-frame difference method, a revised comprehensive difference method is used, and combined with the special traffic video background, a background updating method revised from Surrender Algorithm is proposed. The moving object tracking algorithm based on matching matrix is explained to focus on the problem of failure of tracking moving objects when each of them are kept out. The application of software demonstrates that the method cited in this paper proves to be right and feasible and meet the need of highway operation monitor.

scholarly journals Soccer Players Detection Using GDLS Optimization and Spatial Bitwise Operation Filter

2019 ◽  
Vol 2 (1) ◽  
pp. 49-58
Author(s):  
Adhi Dharma Wibawa ◽  
Atyanta Nika Rumaksari
Keyword(s):  
Computer Vision ◽  
Background Subtraction ◽  
Spatial Filter ◽  
Moving Object ◽  
Experimental Result ◽  
Digital Arithmetic ◽  
Sport Industry ◽  
Arithmetic Algorithm ◽  
Computer Vision Technology ◽  
Player Detection

Advancement computer vision technology in order to help coach creates strategy has been affecting the sport industry evolving very fast. Players movement patterns and other important behavioral activities regarding the tactics during playing the game are the most important data obtained in applying computer vision in Sport Industry. The basic technique for extracting those information during the game is player detection. Three fundamental challenges of computer vision in detecting objects are random object’s movement, noise and shadow. Background subtraction is an object’s detection method that used widely for separating moving object as foreground and non moving object as background. This paper proposed a method for removing shadow and unwanted noise by improving traditional background subtraction technique. First, we employed GDLS algorithm to optimize background-foreground separation. Then, we did filter shadows and crumbs-like object pixels by applying digital spatial filter which is created from implementation of digital arithmetic algorithm (bitwise operation). Finally, our experimental result demonstrated that our algorithm outperform conventional background subtraction algorithms. The experiments result proposed method has obtained 80.5% of F1-score with average 20 objects were detected out of 24 objects.

scholarly journals Perbandingan Metode Sobel, Prewitt, Robert dan Canny pada Deteksi Tepi Objek Bergerak

2020 ◽  
Vol 12 (2) ◽  
pp. 112-120
Author(s):  
Wahyu Supriyatin
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Moving Object ◽  
Video Object ◽  
Test Results ◽  
Detection Algorithms ◽  
Canny Algorithm

Computer vision is one of field of image processing. To be able to recognize a shape, it requires the initial stages in image processing, namely as edge detection. The object used in tracking in computer vision is a moving object (video). Edge detection is used to recognize edges of objects and reduce existing noise. Edge detection algorithms used for this research are using Sobel, Prewitt, Robert and Canny. Tests were carried out on three videos taken from the Matlab library. Testing is done using Simulik Matlab tools. The edge and overlay test results show that the Prewitt algorithm has better edge detection results compared to other algorithms. The Prewitt algorithm produces edges whose level of accuracy is smoother and clearer like the original object. The Canny algorithm failed to produce an edge on the video object. The Sobel and Robert algorithm can detect edges, but it is not clear as Prewitt does, because there are some missing edges.

scholarly journals Individual Detection and Tracking of Group Housed Pigs in Their Home Pen Using Computer Vision

2021 ◽  
Vol 2 ◽  
Author(s):  
Lisette. E. van der Zande ◽  
Oleksiy Guzhva ◽  
T. Bas Rodenburg
Keyword(s):  
Computer Vision ◽  
Detection Algorithm ◽  
Enriched Environment ◽  
Tracking Algorithm ◽  
Individual Activity ◽  
Detection Model ◽  
Detection And Tracking ◽  
Tracking By Detection ◽  
Enriched Environments ◽  
The Individual

Modern welfare definitions not only require that the Five Freedoms are met, but animals should also be able to adapt to changes (i. e., resilience) and reach a state that the animals experience as positive. Measuring resilience is challenging since relatively subtle changes in animal behavior need to be observed 24/7. Changes in individual activity showed potential in previous studies to reflect resilience. A computer vision (CV) based tracking algorithm for pigs could potentially measure individual activity, which will be more objective and less time consuming than human observations. The aim of this study was to investigate the potential of state-of-the-art CV algorithms for pig detection and tracking for individual activity monitoring in pigs. This study used a tracking-by-detection method, where pigs were first detected using You Only Look Once v3 (YOLOv3) and in the next step detections were connected using the Simple Online Real-time Tracking (SORT) algorithm. Two videos, of 7 h each, recorded in barren and enriched environments were used to test the tracking. Three detection models were proposed using different annotation datasets: a young model where annotated pigs were younger than in the test video, an older model where annotated pigs were older than the test video, and a combined model where annotations from younger and older pigs were combined. The combined detection model performed best with a mean average precision (mAP) of over 99.9% in the enriched environment and 99.7% in the barren environment. Intersection over Union (IOU) exceeded 85% in both environments, indicating a good accuracy of the detection algorithm. The tracking algorithm performed better in the enriched environment compared to the barren environment. When false positive tracks where removed (i.e., tracks not associated with a pig), individual pigs were tracked on average for 22.3 min in the barren environment and 57.8 min in the enriched environment. Thus, based on proposed tracking-by-detection algorithm, pigs can be tracked automatically in different environments, but manual corrections may be needed to keep track of the individual throughout the video and estimate activity. The individual activity measured with proposed algorithm could be used as an estimate to measure resilience.

Moving Object Detection and Tracking Algorithm Based on Background Subtraction

2012 ◽  
Vol 263-266 ◽  
pp. 2211-2216
Author(s):  
Qing Ye ◽  
Yong Mei Zhang
Keyword(s):  
Object Detection ◽  
Background Subtraction ◽  
Moving Object Detection ◽  
Moving Object ◽  
Tracking Algorithm ◽  
Difference Image ◽  
Detection And Tracking ◽  
The Difference ◽  
Background Image ◽  
Static Background

Moving target detection and tracking algorithm as the core issue of computer vision and human-computer interaction is the first step of intelligent video surveillance system. Through comparing temporal difference method and background subtraction, a moving object detection and tracking algorithm based on background subtraction under static background is proposed, in order to quickly and accurately detect and identify the moving object in the intelligent monitoring system. In this algorithm, firstly, we use background acquisition method to receive the background image, then use the current frame image and the received background image to perform background subtraction in order to extract foreground object information and receive the difference image; secondly, we use threshold segmentation and morphology image processing to process the difference image in order to eliminate noises and receive the clear binary moving object image; finally, we use the centroid tracking method to track and mark the moving object. Experimental results show that the algorithm can effectively and quickly detect and track moving object from video sequence under static background. This algorithm is easily realized and has good real-time and robust, which is automated and self triggered for background updating. The algorithm can be used in driver assistance systems, motion capture, virtual reality and other fields.

scholarly journals IMPROVED BIOLOGICAL IMAGE TRACKING ALGORITHM OF ATHLETE’S CERVICAL SPINE HEALTH

2021 ◽  
Vol 27 (3) ◽  
pp. 274-277
Author(s):  
Chunmin Dai ◽  
Yang Lu
Keyword(s):  
Cervical Spine ◽  
Tracking Algorithm ◽  
Treatment Results ◽  
Level Of Evidence ◽  
Image Tracking ◽  
Detection And Tracking ◽  
Camshift Algorithm ◽  
Center Position ◽  
Feedback Algorithm ◽  
Biological Image

ABSTRACT Introduction This paper research an improved biological image tracking algorithm of athlete’s cervical spine health under color feedback. Objective A new algorithm is proposed to improve the accuracy of detection and tracking. Methods In this study, the first thing is to apply the color feedback algorithm to improve and optimize the Improved Camshift algorithm. The optimized algorithm was used to track the center of the image, and the video was processed frame by frame. The center position of the tracking frame was obtained. Results The average number of head twists per person is 39 times. Among the three groups, children twisted the least, and older adults twisted the most. Conclusion The algorithm proposed in this study has certain effectiveness and superiority and can be well applied to detecting the number of head twists during exercise. Level of evidence II; Therapeutic studies - investigation of treatment results.

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