3D shape descriptor for objects recognition

Sales, Daniel Oliva; Amaro, Jean; Osório, Fernando Santos

doi:10.1109/sbr-lars-r.2017.8215285

Cited by 4 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To generate a standard entry with recognizable patterns for the RNA, the so-called "object signature", 3D-Contour Sample Distances method [13] has been applied. The features extracted by the 3D-CSD are based on the principle that each object class has a unique 3D outline appearance.…”

Section: Theorical Background and Proposed Model 41 Extraction Of 3mentioning

confidence: 99%

Computer Vision System with 2D and 3D Data Fusion for Detection of Possible Auxiliaries Routes in Stretches of Interdicted Roads

Bruno¹,

Nunes²,

Amaro³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

Self Cite

View full text Add to dashboard Cite

In this paper we present an intelligent system to help autonomous vehicles in real cities and with local traffic rules. A 2D and 3D visual attention system is proposed, capable of detecting the use of signs and aids in cases of major roadblock (road under work, with a traffic accident, etc.). For this to be possible, we analyze the cones and traffic signs that usually alert a driver about this type of problem. The main objective is to provide support for autonomous vehicles to be able to find an auxiliary route that is not previously mapped. For this we use a Grid Point Cloud Map. Using the ORB-SLAM visual odometry system we can correctly fit each stereo frame point cloud in the pose where the images were collected. With the concatenation of point clouds generated by the stereo camera, every grid block can draw the main characteristics of its region and an auxiliary route can be mapped. In this type of situation the vision system must work in real time. The results are promising and very satisfactory, we obtained an accuracy of 98.4% in the 2D classification task and 83% accuracy in the single frame 3D detection task.

show abstract

Section: Theorical Background and Proposed Model 41 Extraction Of 3mentioning

confidence: 99%