A Cloud Infrastructure for Target Detection and Tracking Using Audio and Video Fusion
Abstract
This paper presents a Cloud-based architecture for detecting and tracking multiple moving targets from airborne videos combined with the audio assistance, which is called Cloud-based Audio-Video (CAV) fusion. The CAV system innovation is a method for user-based voice-to-text color feature descriptor track matching with an automated hue feature extraction from image pixels. The introduced CAV approach is general purpose for detecting and tracking different valuable targets' movement for suspicious behavior recognition through multi-intelligence data fusion. Using Cloud computing leads to real-time performance as compared a single machine workflow. The obtained multiple moving target tracking results from airborne videos demonstrate that the CAV approach provides improved frame rate, enhanced detection, and real-time tracking and classification performance under realistic conditions.
Cite
Text
Liu et al. "A Cloud Infrastructure for Target Detection and Tracking Using Audio and Video Fusion." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015. doi:10.1109/CVPRW.2015.7301299Markdown
[Liu et al. "A Cloud Infrastructure for Target Detection and Tracking Using Audio and Video Fusion." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2015.](https://mlanthology.org/cvprw/2015/liu2015cvprw-cloud/) doi:10.1109/CVPRW.2015.7301299BibTeX
@inproceedings{liu2015cvprw-cloud,
title = {{A Cloud Infrastructure for Target Detection and Tracking Using Audio and Video Fusion}},
author = {Liu, Kui and Liu, Bingwei and Blasch, Erik and Shen, Dan and Wang, Zhonghai and Ling, Haibin and Chen, Genshe},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2015},
pages = {74-81},
doi = {10.1109/CVPRW.2015.7301299},
url = {https://mlanthology.org/cvprw/2015/liu2015cvprw-cloud/}
}