A Method of Effective Background Estimation for Video Sequences with Dense Moving Objects

2015 ◽  
Author(s):  
Hui Zhu ◽  
N. E. Mastorakis ◽  
X. D. Zhuang
Keyword(s):  
Moving Objects ◽  
Video Sequences ◽  
Background Estimation

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.

scholarly journals Object detection and tracking in video sequences: formalization, metrics and results

Informatics ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 43-60
Author(s):  
R. P. Bohush ◽  
S. V. Ablameyko
Keyword(s):  
Moving Objects ◽  
Human Movement ◽  
Video Sequences ◽  
Multiple Objects ◽  
Movement Trajectories ◽  
Detection And Tracking ◽  
Form Features ◽  
One And Many ◽  
Tracking Objects

One of the promising areas of development and implementation of artificial intelligence is the automatic detection and tracking of moving objects in video sequence. The paper presents a formalization of the detection and tracking of one and many objects in video. The following metrics are considered: the quality of detection of tracked objects, the accuracy of determining the location of the object in a frame, the trajectory of movement, the accuracy of tracking multiple objects. Based on the considered generalization, an algorithm for tracking people has been developed that uses the tracking through detection method and convolutional neural networks to detect people and form features. Neural network features are included in a composite descriptor that also contains geometric and color features to describe each detected person in the frame. The results of experiments based on the considered criteria are presented, and it is experimentally confirmed that the improvement of the detector operation makes it possible to increase the accuracy of tracking objects. Examples of frames of processed video sequences with visualization of human movement trajectories are presented.

Research on Moving Human Positioning in Complex Background Video

2011 ◽  
Vol 225-226 ◽  
pp. 403-406
Author(s):  
Xin Zhang ◽  
Xiao Tao Wang ◽  
Bing Wang ◽  
Yue Hua Gao
Keyword(s):  
Skin Color ◽  
Video Sequence ◽  
Moving Objects ◽  
Video Sequences ◽  
Subtraction Method ◽  
Fusion Model ◽  
Complex Background ◽  
Illumination Changes ◽  
Hsv Color Model ◽  
Human Object

Human Skin Color(HSC) features have been widely used in video moving human positioning. However, in complex background video sequences, due to illumination changes or other moving objects which have similar HSC regions, the effect of moving human positioning is not satisfactory. A new method of moving human positioning applied on complex background video sequences is presented in this paper. Firstly, brightness information of the video sequence images is detected and analyzed based on HSV color model. Secondly, adopt the multi frame subtraction method to extract the moving object regions from motionless background. Then, the regions with distinctive HSC features are separated from other moving objects using the data fusion model of HSC and brightness information. Finally, identify human object among regions with HSC features according to the prior knowledge of human. The experimental results show that the method provided in this paper is effective in moving human positioning of complex background video, and has the strong illumination change adaptability and anti-jamming ability.

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