Detection of moving objects in video sequences by the computation of optical flow based on region growing

2011 ◽  
Vol 21 (2) ◽  
pp. 283-286 ◽  
Author(s):  
A. Kravchonok
Keyword(s):  
Optical Flow ◽  
Moving Objects ◽  
Region Growing ◽  
Video Sequences

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.

Adaptive Background Image Calculation for Video Sequences Based on Optical Flow

2014 ◽  
Vol 945-949 ◽  
pp. 1820-1824
Author(s):  
Hui Zhu ◽  
Xiao Peng Ji
Keyword(s):  
Optical Flow ◽  
Video Processing ◽  
Moving Objects ◽  
Averaging Method ◽  
Moving Object ◽  
New Method ◽  
Video Sequences ◽  
Background Estimation ◽  
Traffic Video ◽  
Background Image

A new method is proposed to calculate the background in video sequences. The optical flow is estimated to determine the local regions occupied by moving objects. The background image is calculated by an efficient averaging process excluding the moving object regions, which overcomes the foreground-occluding problem in direct averaging method for background estimation. The experiments for traffic video processing prove the method’s effectiveness and robustness.

Object Tracking Based on Dual-View Stereo System

2013 ◽  
Vol 850-851 ◽  
pp. 780-783
Author(s):  
Jian De Fan ◽  
Jiang Bo Zhu
Keyword(s):  
Computer Vision ◽  
Optical Flow ◽  
Object Tracking ◽  
Moving Objects ◽  
Current Method ◽  
Research Area ◽  
Tracking Algorithm ◽  
Video Sequences ◽  
Feature Points ◽  
Motion Trajectories

Tracking moving objects in dual-view stereo system is becoming a hot research area in computer vision. To capture the moving objects pixels more accurately, we proposed a new object tracking algorithm which first compute moving objects feature points and then match these points, finally connect the matching feature points and get objects motion trajectories. The algorithm was tested in the video sequences with resolution 640×480 and 768×576 individually. The results show that the algorithm is more robust and the trajectories of the moving objects tracked with our method are more accurate compared with current method of L-K optical flow.

Research on Information Technology with Moving Object Segmentation Based on Memory Matrix and Kalman Filter

2013 ◽  
Vol 859 ◽  
pp. 482-485
Author(s):  
Can Can Zhou ◽  
Xiao Run Li
Keyword(s):  
Information Technology ◽  
Kalman Filter ◽  
Moving Objects ◽  
Object Segmentation ◽  
Region Growing ◽  
Moving Object ◽  
Video Sequences ◽  
Multiple Channels ◽  
Segmentation Method ◽  
Moving Object Segmentation

In this paper,information technology is introduced briefly and in order to avoid inaccurate segmentation of moving objects caused by object holes and ghost, an automatic moving object segmentation method which belongs to information technology based on memory matrix and Kalman filter theory is proposed. Memory matrix is used in multiple channels to extract the initial background, and ghost is eliminated after updating background according to the theory of Kalman filter. Moving objects are extracted using adaptive threshold, and object segmentation is achieved by improved region growing method on base of block processing. The experimental results indicate that the proposed algorithm can accurately segment moving object from video sequences, and has very good robustness against illumination variance and moving noise.

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.

Robust Autonomous Car-Like Robot Tracking Based on Tracking-Learning-Detection

2014 ◽  
Vol 687-691 ◽  
pp. 564-571 ◽  
Author(s):  
Lin Bao Xu ◽  
Shu Ming Tang ◽  
Jin Feng Yang ◽  
Yan Min Dong
Keyword(s):  
Optical Flow ◽  
Fast Moving ◽  
Moving Objects ◽  
Tracking Algorithm ◽  
Robust Tracking ◽  
Symmetry Detection ◽  
Tracking Method ◽  
Robot Tracking ◽  
Improve Accuracy ◽  
First Time

This paper proposes a robust tracking algorithm for an autonomous car-like robot, and this algorithm is based on the Tracking-Learning-Detection (TLD). In this paper, the TLD method is extended to track the autonomous car-like robot for the first time. In order to improve accuracy and robustness of the proposed algorithm, a method of symmetry detection of autonomous car-like robot rear is integrated into the TLD. Moreover, the Median-Flow tracker in TLD is improved with a pyramid-based optical flow tracking method to capture fast moving objects. Extensive experiments and comparisons show the robustness of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document