Article | 30-November-2020
. Collision detection algorithms can be roughly divided into two categories: time domain and space domain . Bounding box is a kind of detection algorithm based on spatial domain. It has the advantages of high detection efficiency and simple detection process .
For the complex model to be tested, the thought of bounding box is to surround the model to be tested with simple bounding box whose volume is slightly larger than the model to be tested and whose geometric features are regular . When
International Journal of Advanced Network, Monitoring and Controls, Volume 6 , ISSUE 1, 18–23
Article | 07-May-2018
Collision detection plays a vital role in improving the sense of immersion and realism in virtual environment. The bounding box is the most basic collision detection algorithm, The OBB intersect test tightness and can able to significantly reduce the number of bounding volume. and try to occupy less storage space. Propose a bounding box of direction cylindrical So it can be improved though surrounded objects more close, then improve the efficiency of collision detection.
International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 2, 44–47
Article | 07-May-2018
This paper take the development of virtual campus roaming system as an example. Aiming at the defects of the different kinds of bounding boxes collision detection technology, analyzes the advantages of the hybrid hierarchical bounding box collision detection algorithm based on spheres and oriented bounding box, and described the way to construct it. Finally, the algorithm is tested in the VC and Unity3d engine, a good result is obtained.
International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 2, 84–88
Article | 13-July-2020
algorithm. The detection process of Faster R-CNN model can be shown in Figure 1. First, input images of any size and corresponding annotation files into the network model. Second, they will go through the convolution layer to extract the features of the input image. Third, use RPN for region prediction, and use the ROI mapping operation to map the predicted candidate frame to the feature map. Finally, identify the target category and locate the bounding box. It can be seen that in the Faster R-CNN
International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 2, 76–82