Region of Interest Constrained Negative Obstacle Detection and Tracking With a Stereo Camera

2022 ◽  
pp. 1-1
Author(s):  
Tian Sun ◽  
Wei Pan ◽  
Yujie Wang ◽  
Yong Liu
Keyword(s):  
Region Of Interest ◽  
Obstacle Detection ◽  
Stereo Camera ◽  
Detection And Tracking ◽  
Negative Obstacle

Unmanned vehicle dynamic obstacle detection, tracking and recognition method based on laser sensor

2021 ◽  
Vol 14 (2) ◽  
pp. 239-251
Author(s):  
Hualei Zhang ◽  
Mohammad Asif Ikbal
Keyword(s):  
Feature Fusion ◽  
Obstacle Detection ◽  
Geometric Features ◽  
Recognition Method ◽  
Content Type ◽  
Detection And Tracking ◽  
Spatio Temporal ◽  
Dynamic Obstacle ◽  
Real Vehicle ◽  
Temporal Feature

PurposeIn response to these shortcomings, this paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.Design/methodology/approachThe existing dynamic obstacle detection and tracking methods based on geometric features have a high false detection rate. The recognition methods based on the geometric features and motion status of dynamic obstacles are greatly affected by distance and scanning angle, and cannot meet the requirements of real traffic scene applications.FindingsFirst, based on the geometric features of dynamic obstacles, the obstacles are considered The echo pulse width feature is used to improve the accuracy of obstacle detection and tracking; second, the space-time feature vector is constructed based on the time dimension and space dimension information of the obstacle, and then the support vector machine method is used to realize the recognition of dynamic obstacles to improve the obstacle The accuracy of object recognition. Finally, the accuracy and effectiveness of the proposed method are verified by real vehicle tests.Originality/valueThe paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors. The accuracy and effectiveness of the proposed method are verified by real vehicle tests.

Negative Obstacle Detection Using LiDAR Sensors for a Robotic Wheelchair

2018 ◽  
Author(s):  
Taylor E. Baum ◽  
Kelilah L. Wolkowicz ◽  
Joseph P. Chobot ◽  
Sean N. Brennan
Keyword(s):  
Time Algorithm ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Detection Accuracy ◽  
Time Varying ◽  
Wheelchair Users ◽  
Development Platform ◽  
Robotic Wheelchairs ◽  
Robotic Wheelchair ◽  
Negative Obstacle

The objective of this work is to develop a negative obstacle detection algorithm for a robotic wheelchair. Negative obstacles — depressions in the surrounding terrain including descending stairwells, and curb drop-offs — present highly dangerous navigation scenarios because they exhibit wide characteristic variability, are perceptible only at close distances, and are difficult to detect at normal operating speeds. Negative obstacle detection on robotic wheelchairs could greatly increase the safety of the devices. The approach presented in this paper uses measurements from a single-scan laser range-finder and a microprocessor to detect negative obstacles. A real-time algorithm was developed that monitors time-varying changes in the measured distances and functions through the assumption that sharp increases in this monitored value represented a detected negative obstacle. It was found that LiDAR sensors with slight beam divergence and significant error produced impressive obstacle detection accuracy, detecting controlled examples of negative obstacles with 88% accuracy for 6 cm obstacles and above on a robotic development platform and 90% accuracy for 7.5 cm obstacles and above on a robotic wheelchair. The implementation of this algorithm could prevent life-changing injuries to robotic wheelchair users caused by negative obstacles.

Adaptive Region of Interest (ROI) detection and tracking for respiration measurement in thermal video

2012 ◽  
Author(s):  
Balvinder Kaur ◽  
Jill K. Nelson ◽  
Timothy Williams ◽  
Barbara L. O'Kane
Keyword(s):  
Region Of Interest ◽  
Detection And Tracking ◽  
Thermal Video

Obstacle detection and tracking by millimeter wave RADAR

2004 ◽  
Vol 37 (8) ◽  
pp. 322-327 ◽  
Author(s):  
Christophe Blanc ◽  
Romuald Aufrère ◽  
Laurent Malaterre ◽  
Jean Gallice ◽  
Joseph Alizon
Keyword(s):  
Millimeter Wave ◽  
Obstacle Detection ◽  
Millimeter Wave Radar ◽  
Detection And Tracking ◽  
Wave Radar

Computer Vision Based Asset Surveillance for Smart Buildings

2020 ◽  
Vol 17 (1) ◽  
pp. 456-463
Author(s):  
K. S. Gautam ◽  
Latha Parameswaran ◽  
Senthil Kumar Thangavel
Keyword(s):  
Computer Vision ◽  
Correlation Coefficient ◽  
Real World ◽  
Vision System ◽  
Region Of Interest ◽  
Detection And Tracking ◽  
Novel Approach ◽  
Ip Camera ◽  
Pros And Cons ◽  
Real World Problems

Unraveling meaningful pattern form the video offers a solution to many real-world problems, especially surveillance and security. Detecting and tracking an object under the area of video surveillance, not only automates the security but also leverages smart nature of the buildings. The objective of the manuscript is to detect and track assets inside the building using vision system. In this manuscript, the strategies involved in asset detection and tracking are discussed with their pros and cons. In addition to it, a novel approach has been proposed that detects and tracks the object of interest across all the frames using correlation coefficient. The proposed approach is said to be significant since the user has an option to select the object of interest from any two frames in the video and correlation coefficient is calculated for the region of interest. Based on the arrived correlation coefficient the object of interest is tracked across the rest of the frames. Experimentation is carried out using the 10 videos acquired from IP camera inside the building.

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