MPEG-4 video object-based rate allocation with variable temporal rates

2002 ◽  
Vol 38 (19) ◽  
pp. 1088 ◽  
Author(s):  
Jeong-Woo Lee ◽  
A. Vetro ◽  
Yao Wang ◽  
Yo-Sung Ho
Keyword(s):  
Rate Allocation ◽  
Video Object ◽  
Object Based

Video Object Segmentation

2009 ◽  
pp. 809-816 ◽  
Author(s):  
Ee Ping Ong ◽  
Weisi Lin
Keyword(s):  
Object Segmentation ◽  
Research Effort ◽  
Complete Solution ◽  
Cost Savings ◽  
Traffic Monitoring ◽  
Video Object Segmentation ◽  
Video Object ◽  
Home Office ◽  
Practical Applications ◽  
Object Based

Video object segmentation aims to extract different video objects from a video (i.e., a sequence of consecutive images). It has attracted vast interests and substantial research effort for the past decade because it is a prerequisite for visual content retrieval (e.g., MPEG-7 related schemes), object-based compression and coding (e.g., MPEG-4 codecs), object recognition, object tracking, security video surveillance, traffic monitoring for law enforcement, and many other applications. Video object segmentation is a nonstandardized but indispensable component for an MPEG4/7 scheme in order to successfully develop a complete solution. In fact, in order to utilize MPEG-4 object-based video coding, video object segmentation must first be carried out to extract the required video object masks. Video object segmentation is an even more important issue in military applications such as real-time remote missile/vehicle/soldier’s identification and tracking. Other possible applications include home/office/warehouse security where monitoring and recording of intruders/foreign objects, alarming the personnel concerned or/and transmitting the segmented foreground objects via a bandwidth-hungry channel during the appearance of intruders are of particular interest. Thus, it can be seen that fully automatic video object segmentation tool is a very useful tool that has very wide practical applications in our everyday life where it can contribute to improved efficiency, time, manpower, and cost savings.

Research of Object-Oriented 3D Video Coding Segmentation

2013 ◽  
Vol 303-306 ◽  
pp. 2254-2259
Author(s):  
Jian Zhang ◽  
Zhi Ye Huang ◽  
Jin Xiang Peng
Keyword(s):  
Video Coding ◽  
Change Detection ◽  
High Order ◽  
3D Video ◽  
Video Object ◽  
Disparity Map ◽  
Video Objects ◽  
Object Based ◽  
3D Video Coding ◽  
Statistical Change

This paper presents an algorithm about the extraction of left channel video object which based on high-order statistical change detection and the segmentation of right channel video object based on parallax matching. This algorithm combines the advantages of disparity map segmentation and multiple frame difference motion segmentation. First of all, through the segmentation of the parallax matching video objects in the right channel, we can get primary partition templates in different layers of parallax targets; Secondary with high-order statistical change detection, we can extract video objects in the left channel from templates. Finally we obtain the accurate moving target. Based on 3D multi-view video segmentation, we use H.264-based method to encode the main image flow and then get object-based 3D video coding

Object-based rate allocation with spatio-temporal trade-offs

2002 ◽  
Author(s):  
Jeong-Woo Lee ◽  
Anthony Vetro ◽  
Yao Wang ◽  
Yo-Sung Ho
Keyword(s):  
Rate Allocation ◽  
Trade Offs ◽  
Object Based ◽  
Spatio Temporal

Tracking Video Object Based on Central Macroblocks

2011 ◽  
Vol 34 (9) ◽  
pp. 1712-1718
Author(s):  
Guo-Qiang XIAO ◽  
Qin KANG ◽  
Jian-Min JIANG ◽  
Bei-Bei ZHANG
Keyword(s):  
Video Object ◽  
Object Based

Color Recognition of Video Object Based on HSV Model

2011 ◽  
Vol 143-144 ◽  
pp. 721-725
Author(s):  
Zhao Quan Cai ◽  
Wei Luo ◽  
Zhong Nan Ren ◽  
Han Huang
Keyword(s):  
Real Time ◽  
Moving Objects ◽  
Target Identification ◽  
Target Color ◽  
Video Object ◽  
Color Recognition ◽  
Video Images ◽  
Object Based ◽  
Color Identification ◽  
Color Judgment

In the presented paper, we proposed a common color model and designed the color judgment method, which is based on the HSV model. This method will translate the RGB values of the points in video images to HSV values, and use HSV values to recognize the color. After that, software of real-time video object recognition was developed based on color features, which is also based on their search of target color identification. Besides, the system is developed by VC based on OpenCV, which has achieved the goal of real-time video motion detection and object color recognition. Finally, the experimental results indicate that the algorithm is accurate and similar to human recognition of the moving objects in videos view, which demonstrates the good performance of the target identification and color judgment.

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