Video inpainting model for camera motion based on improved background subtraction method

2013 ◽  
Author(s):  
Sun Xiaoming ◽  
Yu Xiaoyang ◽  
Yu Shuchun ◽  
Guan Yanxia ◽  
Meng Xiaoliang ◽  
...  
Keyword(s):  
Background Subtraction ◽  
Camera Motion ◽  
Subtraction Method ◽  
Video Inpainting

Video inpainting using object motion rate and color variance in spatiotemporal domain

2020 ◽  
pp. 1-14
Author(s):  
Hui-Yu Huang ◽  
Chih-Hung Lin
Keyword(s):  
Reference Frame ◽  
Background Subtraction ◽  
Object Motion ◽  
Moving Object ◽  
Subtraction Method ◽  
Digital Videos ◽  
Rate Estimation ◽  
Model Based ◽  
Video Inpainting

Inpainting is a technique to enhance digital videos. Based on the spatiotemporal domain, we herein propose a video inpainting method to repair the removal objects in the videos. The method consists of an adaptive foreground model, the motion rate estimation of objects, and a repairing scheme. Initially, the adaptive foreground model based on the background subtraction method is developed. The model is used to estimate the motion rate for each moving object in the frame. According to the estimated motion rate, the model specifies an adaptive interval between the forwarding reference frame and backward reference frame to obtain the useful information and to repair the removal objects. The remaining un-repaired areas are filled using an exemplar-based inpainting technique with color variance. The results show that the proposed method can produce visually pleasing results. Additionally, it reduces the inpainting time and provides efficient computing.

A novel background subtraction method based on color invariants

2013 ◽  
Vol 117 (11) ◽  
pp. 1589-1597 ◽  
Author(s):  
Hong Zhou ◽  
Yiru Chen ◽  
Rong Feng
Keyword(s):  
Background Subtraction ◽  
Subtraction Method

scholarly journals Moving Object Tracking Algorithm for Complex Shape and Occlusion Handling

2019 ◽  
Vol 9 (1) ◽  
pp. 6299-6304
Keyword(s):  
Object Tracking ◽  
Video Surveillance ◽  
Background Subtraction ◽  
Traffic Monitoring ◽  
Block Matching ◽  
Moving Object ◽  
Subtraction Method ◽  
Matching Algorithm ◽  
Block Matching Algorithm ◽  
Moving Object Tracking

An object tracking increases loads of enthusiasm for dynamic research in applications such as video surveillance, vehicle navigation, highways, crowded public places, borders, forest and traffic monitoring areas. The system we develop aims to measure and analyze the application of background subtraction method and block matching algorithm to trace object movements through video-based. The making of video surveillance systems “smart” requires fast, reliable and robust algorithms for moving object detection and tracking. 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.

Block Background Subtraction Method Based on ViBe

2014 ◽  
Vol 556-562 ◽  
pp. 3549-3552
Author(s):  
Lian Fen Huang ◽  
Qing Yue Chen ◽  
Jin Feng Lin ◽  
He Zhi Lin
Keyword(s):  
Spatial Correlation ◽  
Background Subtraction ◽  
Video Sequence ◽  
Moving Objects ◽  
Moving Object ◽  
Background Model ◽  
Subtraction Method ◽  
Current Frame ◽  
The Difference ◽  
Set Up

The key of background subtraction which is widely used in moving object detecting is to set up and update the background model. This paper presents a block background subtraction method based on ViBe, using the spatial correlation and time continuity of the video sequence. Set up the video sequence background model firstly. Then, update the background model through block processing. Finally employ the difference between the current frame and background model to extract moving objects.

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