scholarly journals A novel approach for object extraction from video sequences based on continuous background/foreground classification

2010 ◽  
Author(s):  
T C Bellardi ◽  
J Rios-Martinez ◽  
D Vasquez ◽  
C Laugier
Keyword(s):  
Video Sequences ◽  
Object Extraction ◽  
Novel Approach ◽  
Continuous Background

Robust Feature Points Extraction Based on Harris and SIFT

2013 ◽  
Vol 347-350 ◽  
pp. 3500-3504
Author(s):  
Xiao Ran Guo ◽  
Shao Hui Cui ◽  
Fang Dan
Keyword(s):  
Video Sequence ◽  
State Of The Art ◽  
Image Motion ◽  
Video Sequences ◽  
Stabilization System ◽  
Feature Points ◽  
Image Stabilization ◽  
Current State ◽  
Novel Approach ◽  
Key Points

This article presents a novel approach to extract robust local feature points of video sequence in digital image stabilization system. Robust Harris-SIFT detector is proposed to select the most stable SIFT key points in the video sequence where image motion is happened due to vehicle or platform vibration. Experimental results show that the proposed scheme is robust to various transformations of video sequences, such as translation, rotation and scaling, as well as blurring. Compared with the current state-of-the-art schemes, the proposed scheme yields better performances.

scholarly journals Object extraction in video sequences based on spatiotemporal independent component analysis

2021 ◽  
Author(s):  
Zhenhe Chen
Keyword(s):  
Independent Component Analysis ◽  
Video Processing ◽  
Moving Objects ◽  
Research Work ◽  
Region Growing ◽  
Component Analysis ◽  
Independent Component ◽  
Video Sequences ◽  
Object Extraction ◽  
Surveillance Systems

Video object extration is one of the most important areas of video processing in which objects from video sequences are extracted and used for many applications such as surveillance systems, pattern recognition etc. In this research work, an object-based technique based on the spatiotemporal independent component analysis (stICA) is developed to extract moving objects from video sequences. Using the stICA, the preliminary source images containing moving objects in the video sequence are extracted. These images are processed using wavelet analysis, edge detection, region growing and multiscale segmentation techniques to improve the accuracy of the extracted objects. A novel compensation method is applied to deal with the nonlinear problem caused by the application of the stICA directly to the video sequences. The recovered objects are indexed by the singular calue decompensation (SVD) and linear combination analysis. Simulation results demonstrate the effectiveness of the stICA-based object extraction technique in content-based video processing applications.

scholarly journals Computer Vision-based Human Activity Recognition for Elderly Home Care

2019 ◽  
Vol 9 (1S) ◽  
pp. 299-303
Keyword(s):  
Computer Vision ◽  
Elderly People ◽  
Orientation Angle ◽  
Major Axis ◽  
Gaussian Mixture ◽  
Minor Axis ◽  
Video Sequences ◽  
Novel Approach ◽  
Human Object ◽  
Axis Length

Video-based monitoring of elderly people at home receives more attention in recent days. In this paper, we propose a novel approach to develop smart monitoring system for elderly people using computer vision techniques. Gaussian Mixture Model (GMM) based algorithm is used for background and foreground separation inorder to track the activities of human object. The minimum bounding box of the human object is traced and features like major axis length, minor axis length and orientation angle are extracted. The proposed approach is evaluated on the video sequences of fall dataset.

scholarly journals Detection and Tracking of Lane Crossing Vehicles in Traffic Video for Abnormality Analysis

2021 ◽  
Vol 10 (4) ◽  
pp. 1-9
Author(s):  
Arun Kumar H D ◽  
Prabhakar C J
Keyword(s):  
Geometrical Structure ◽  
Experimental Results ◽  
Video Sequences ◽  
The Road ◽  
Model Based ◽  
Detection And Tracking ◽  
Novel Approach ◽  
Traffic Video

In this paper, we present a novel approach for detection and tracking of lane crossing/illegal lane crossing vehicles in traffic video of urban highways. For that intention, an initial pace is performed that estimates the road region of the geometrical structure. After finding the road region, every vehicle is tracked in order to detect lane crossing vehicles according to the distance between lane lines and vehicle centre, it is followed by tracking of lane crossing vehicles based on model-based strategy. The proposed system has been evaluated using recall and precision metric, which are received using experiments carried on selected video sequences of GRAM-RTM dataset and publically available video sequences. The experimental results present that our method reaches the highest accuracy for detection of vehicles and tracking of lane crossing vehicles.

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