scholarly journals Fast boosting trees for classification, pose detection, and boundary detection on a GPU

2011 ◽  
Author(s):  
Neil Birkbeck ◽  
Michal Sofka ◽  
S. Kevin Zhou
Keyword(s):  
Boundary Detection ◽  
Pose Detection

Boundary Detection Using Open Spline Curve Based on Mumford-Shah Model

2009 ◽  
Vol 35 (2) ◽  
pp. 132-136 ◽  
Author(s):  
Xiao-Mao LI ◽  
Lin-Lin ZHU ◽  
Yan-Dong TANG
Keyword(s):  
Boundary Detection ◽  
Spline Curve

An Efficient Shot Boundary Detection Using Data-cube Searching Technique

2020 ◽  
Vol 13 (4) ◽  
pp. 798-807
Author(s):  
J. Kavitha ◽  
P. Arockia Jansi Rani ◽  
P. Mohamed Fathimal ◽  
Asha Paul
Keyword(s):  
Texture Features ◽  
Boundary Detection ◽  
Data Cube ◽  
Multimedia Data ◽  
Shot Boundary Detection ◽  
Gradual Transition ◽  
Detection Rates ◽  
Effective Manner ◽  
Shot Boundary ◽  
Shot Transitions

Background:: In the internet era, there is a prime need to access and manage the huge volume of multimedia data in an effective manner. Shot is a sequence of frames captured by a single camera in an uninterrupted space and time. Shot detection is suitable for various applications such that video browsing, video indexing, content based video retrieval and video summarization. Objective:: To detect the shot transitions in the video within a short duration. It compares the visual features of frames like correlation, histogram and texture features only in the candidate region frames instead of comparing the full frames in the video file. Methods: This paper analyses candidate frames by searching the values of frame features which matches with the abrupt detector followed by the correct cut transition frame with in the datacube recursively until it detects the correct transition frame. If they are matched with the gradual detector, then it will give the gradual transition ranges, otherwise the algorithm will compare the frames within the next datacube to detect shot transition. Results:: The total average detection rates of all transitions computed in the proposed Data-cube Search Based Shot Boundary Detection technique are 92.06 for precision, 96.92 for recall and 93.94 for f1 measure and the maximum accurate detection rate. Conclusion:: Proposed method for shot transitions uses correlation value for searching procedure with less computation time than the existing methods which compares every single frame and uses multi features such as color, edge, motion and texture features in wavelet domain.

Accurate Boundary Detection and Refinement for Continuous Objects in IoT Sensing Networks

2019 ◽  
Vol 57 (6) ◽  
pp. 93-99 ◽  
Author(s):  
Zhangbing Zhou ◽  
Yaqiang Zhang ◽  
Xuefang Yi ◽  
Chao Chen ◽  
Haodi Ping
Keyword(s):  
Boundary Detection ◽  
Continuous Objects

scholarly journals An Intelligent Baby Monitor with Automatic Sleeping Posture Detection and Notification

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 290-306
Author(s):  
Tareq Khan
Keyword(s):  
Language Translation ◽  
Video Data ◽  
The Body ◽  
Night Vision ◽  
Monitoring Device ◽  
Peace Of Mind ◽  
Image Processing Techniques ◽  
Pose Detection ◽  
Processing Techniques ◽  
Monitoring Devices

Artificial intelligence (AI) has brought lots of excitement to our day-to-day lives. Some examples are spam email detection, language translation, etc. Baby monitoring devices are being used to send video data of the baby to the caregiver’s smartphone. However, the automatic understanding of the data was not implemented in most of these devices. In this research, AI and image processing techniques were developed to automatically recognize unwanted situations that the baby was in. The monitoring device automatically detected: (a) whether the baby’s face was covered due to sleeping on the stomach; (b) whether the baby threw off the blanket from the body; (c) whether the baby was moving frequently; (d) whether the baby’s eyes were opened due to awakening. The device sent notifications and generated alerts to the caregiver’s smartphone whenever one or more of these situations occurred. Thus, the caregivers were not required to monitor the baby at regular intervals. They were notified when their attention was required. The device was developed using NVIDIA’s Jetson Nano microcontroller. A night vision camera and Wi-Fi connectivity were interfaced. Deep learning models for pose detection, face and landmark detection were implemented in the microcontroller. A prototype of the monitoring device and the smartphone app were developed and tested successfully for different scenarios. Compared with general baby monitors, the proposed device gives more peace of mind to the caregivers by automatically detecting un-wanted situations.

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