A Novel Foreground/Background Decision using in Unsupervised Segmentation of Moving Objects in Video Sequences

2006 ◽  
Author(s):  
Tsung-Han Tsai ◽  
Guan-Jun Chen ◽  
Wen-Liang Tzeng
Keyword(s):  
Moving Objects ◽  
Video Sequences ◽  
Unsupervised Segmentation

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.

scholarly journals Automatic foreground detection based on KDE and binary classification

2019 ◽  
Vol 15 (1) ◽  
pp. 517
Author(s):  
Mohammed Lahraichi ◽  
Khalid Housni ◽  
Samir Mbarki
Keyword(s):  
Moving Objects ◽  
Binary Classification ◽  
Video Sequences ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
Dynamic Scene ◽  
Dynamic Background ◽  
New Approach ◽  
Markov Random ◽  
Global Threshold

In the recent decades, several methods have been developed to extract moving objects in the presence of dynamic background. However, most of them use a global threshold, and ignore the correlation between neighboring pixels. To address these issues, this paper presents a new approach to generate a probability image based on Kernel Density Estimation (KDE) method, and then apply the Maximum A Posteriori in the Markov Random Field (MAP-MRF) based on probability image, so as to generate an energy function, this function will be minimized by the binary graph cut algorithm to detect the moving pixels instead of applying a thresholding step. The proposed method was tested on various video sequences, and the obtained results showed its effectiveness in presence of a dynamic scene, compared to other background subtraction models.

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