scholarly journals Flow-Process Foreground Region of Interest Detection Method for Video Codecs

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 16263-16276 ◽  
Author(s):  
Zhewei Zhang ◽  
Tao Jing ◽  
Jingning Han ◽  
Yaowu Xu ◽  
Xuejing Li
Keyword(s):  
Detection Method ◽  
Region Of Interest ◽  
Flow Process ◽  
Video Codecs ◽  
Foreground Region ◽  
Interest Detection

scholarly journals A Model-Based Approach of Foreground Region of Interest Detection for Video Codecs

2019 ◽  
Vol 9 (13) ◽  
pp. 2670
Author(s):  
Zhewei Zhang ◽  
Tao Jing ◽  
Bowen Ding ◽  
Meilin Gao ◽  
Xuejing Li
Keyword(s):  
Video Compression ◽  
Computing Time ◽  
Region Of Interest ◽  
Video Codecs ◽  
Model Based ◽  
Foreground Region ◽  
Wide Range ◽  
Interest Detection ◽  
Different Characteristics ◽  
Fine Tune

Detecting the Region of Interest (ROI) for video clips is a significant and useful technique both in video codecs and surveillance/monitor systems. In this paper, a new model-based detection method is designed which suits video compression codecs by proposing two models: an “inter” and “intra” model. The “inter” model exploits the motion information represented as blocks by global motion compensation approaches while the “intra” model extracts the objects details through objects filtering and image segmentation procedures. Finally, the detection results are formed through a new clustering with fine-tune approach from the “intra” model assisted with the “inter” model. Experimental results show that the proposed method fits well for real-time video codecs and it achieves a good performance both on detection precision and on computing time. In addition, the proposed method is versatile for a wide range of surveillance videos with different characteristics.

scholarly journals Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 590
Author(s):  
Zhenqian Zhang ◽  
Ruyue Cao ◽  
Cheng Peng ◽  
Renjie Liu ◽  
Yifan Sun ◽  
...  
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Detection Method ◽  
Region Of Interest ◽  
Average Error ◽  
Vertical Projection ◽  
Cut Edge ◽  
Fitting Method ◽  
Combine Harvester ◽  
Edge Detection Method

A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop row, judging the target demarcation and getting the feature points, the region of interest (ROI) was automatically gained. Subsequently, the vertical projection was applied to reduce the noise. All the points in the ROI were calculated, and a dividing point was found in each row. The hierarchical clustering method was used to extract the outliers. At last, the polynomial fitting method was used to acquire the straight or curved cut-edge. The results gained from the samples showed that the average error for locating the cut-edge was 2.84 cm. The method was capable of providing support for the automatic navigation of a combine harvester.

Hierarchical region-of-interest detection

2006 ◽  
Vol 45 (7) ◽  
pp. 077201 ◽  
Author(s):  
Huibao Lin
Keyword(s):  
Region Of Interest ◽  
Interest Detection

Pedestrian Detection Based on Histograms of Oriented Gradients in ROI

2012 ◽  
Vol 542-543 ◽  
pp. 937-940
Author(s):  
Ping Shu Ge ◽  
Guo Kai Xu ◽  
Xiu Chun Zhao ◽  
Peng Song ◽  
Lie Guo
Keyword(s):  
Traditional Method ◽  
Detection Method ◽  
Feature Vector ◽  
Pedestrian Detection ◽  
Region Of Interest ◽  
Experimental Results ◽  
Process Time ◽  
Vertical Edge ◽  
Histograms Of Oriented Gradients

To locate pedestrian faster and more accurately, a pedestrian detection method based on histograms of oriented gradients (HOG) in region of interest (ROI) is introduced. The features are extracted in the ROI where the pedestrian's legs may exist, which is helpful to decrease the dimension of feature vector and simplify the calculation. Then the vertical edge symmetry of pedestrian's legs is fused to confirm the detection. Experimental results indicate that this method can achieve an ideal accuracy with lower process time compared to traditional method.

Sign in / Sign up

Export Citation Format

Share Document