Hand Gesture Detection and Tracking Methods Based on Background Subtraction

2014 ◽  
pp. 485-490 ◽  
Author(s):  
Wei Song ◽  
Zixiao Lu ◽  
Jinhong Li ◽  
Jie Li ◽  
Jinqiao Liao ◽  
...  
Keyword(s):  
Background Subtraction ◽  
Hand Gesture ◽  
Detection And Tracking ◽  
Hand Gesture Detection

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.

scholarly journals HAND GESTURE DETECTION USING SEGMENTATION

2021 ◽  
Vol 9 (3) ◽  
Author(s):  
Nishu Sethi ◽  
Shivangi Kaushal ◽  
Neha Bhateja
Keyword(s):  
Hand Gesture ◽  
Hand Gesture Detection

Human hand gesture detection based on EMG signal using ANN

2014 ◽  
Author(s):  
Md. Mehedi Hasan ◽  
Arifur Rahaman ◽  
Md. Faisal Shuvo ◽  
Md. Abu Saleh Ovi ◽  
Md. Mostafizur Rahman
Keyword(s):  
Hand Gesture ◽  
Human Hand ◽  
Emg Signal ◽  
Hand Gesture Detection

Touchless capacitive sensor for hand gesture detection

2011 ◽  
Author(s):  
Fatemeh Aezinia ◽  
YiFan Wang ◽  
Behraad Bahreyni
Keyword(s):  
Capacitive Sensor ◽  
Hand Gesture ◽  
Hand Gesture Detection

scholarly journals Development of a Novel Algorithm for Moving Object Tracking in Video Surveillance

2019 ◽  
Vol 8 (12) ◽  
pp. 3394-3401
Keyword(s):  
Object Tracking ◽  
Video Surveillance ◽  
Background Subtraction ◽  
Block Matching ◽  
Moving Object ◽  
Subtraction Method ◽  
Matching Algorithm ◽  
Block Matching Algorithm ◽  
Detection And Tracking ◽  
Moving Object Tracking

Video surveillance is a process of analyzing video sequences. It involves analysis, interpretation of object behaviors, as well as object detection and tracking. Video processing plays an important role in the industry and computer vision such as online monitoring of assembly processes, video surveillance security system, medical treatment, robot navigation and military, etc. Detection and tracking of human objects is one of the important studies in improving the ability of the surveillance system. The aim of this research work is to measure and analyze the application of background subtraction method and block matching algorithm to trace object movements through video-based. This research applies background subtraction method to detect moving object, assisted with block matching algorithm which aims to get good results on objects that have been detected. Performance evaluation is carried out to determine the various parameters. In this paper author design and develop a novel algorithm for moving object tracking in video surveillance also compares and analyse existing algorithms for moving object tracking. Author main aim to design and develop an algorithm for moving object tracking to handle occlusion and complex object shapes.

scholarly journals Detection and tracking of moving object using modified background subtraction and Kalman filter

2021 ◽  
Vol 11 (1) ◽  
pp. 217
Author(s):  
Jeevith S. H. ◽  
Lakshmikanth S.
Keyword(s):  
Kalman Filter ◽  
Background Subtraction ◽  
Moving Objects ◽  
Adaptive Threshold ◽  
Vital Role ◽  
Moving Object ◽  
Navigation Systems ◽  
Subtraction Technique ◽  
Night Time ◽  
Detection And Tracking

Moving object detection and tracking (MODT) is the major challenging issue in computer vision, which plays a vital role in many applications like robotics, surveillance, navigation systems, militaries, environmental monitoring etc. There are several existing techniques, which has been used to detect and track the moving object in Surveillance system. Therefore it is necessary to develop new algorithm or modified algorithm which is robust to work in both day and night time. In this paper, modified BGS technique is proposed. The video is first converted to number of frames, then these frame are applied to modified background subtraction technique with adaptive threshold which gives detected object. Kalman filter technique is used for tracking the detected object. The experimental results shows this proposed method can efficiently and correctly detect and track the moving objects with less processing time which is compared with existing techniques.

scholarly journals Hand Gesture Recognition With a Novel Particle Filter

2020 ◽  
Author(s):  
David Huerta ◽  
Eric Crawford ◽  
Scott Brown
Keyword(s):  
Kalman Filter ◽  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Real World ◽  
Physical Significance ◽  
Hand Gesture ◽  
Hand Gestures ◽  
Input Devices ◽  
Detection And Tracking ◽  
Marker Detection

Human Computer Interaction (HCI) has been redefined in this era. People want to interact with their devices in such a way that has physical significance in the real world, in other words, they want ergonomic input devices. In this paper, we propose a new method of interaction with computing devices having a consumer grade camera, that uses two colored markers (red and green) worn on tips of the fingers to generate desired hand gestures, and for marker detection and tracking we usedtemplate matching with kalman filter. We have implemented all the usual system commands, i.e., cursor movement, right click, left click, double click, going forward and backward, zoom in and out through different hand gestures. Our system caneasily recognize these gestures and give corresponding system commands. Our system is suitable for both desktop devices and devices where touch screen is not feasible like large screens or projected screens.

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