Name
Abstract | ||
|---|---|---|
In large field of view for open country, the real-time detection and identification of moving objects with high accuracy is a very challenging work due to the excessive amount of data. This paper proposes a novel framework that consists of a coarse-grained detection as well as a fine-grained detection. To solve the problem of noise-induced object fracture during the coarse-grained detection process, we present a low-complexity connected region detection algorithm to extract moving regions. Furthermore, in the fine-grained detection, Deep Convolution Neural Networks are leveraged to detect more precise coordinates and identify the category of objects. To the best of our knowledge, this is the first work that proposes a coarse-to-fine grained framework to detect moving objects on high-resolution scenes. Experimental results show that the proposed framework can robustly work on the high resolution video frames (1920 & x002A;1080p) with complex situations more fastly and accurately over existing methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2020.2972562 | IEEE ACCESS |
Keywords | DocType | Volume |
Connected region detection,deep convolution neural networks,foreground extraction,high resolution,moving object detection | Journal | 8 |
ISSN | Citations | PageRank |
2169-3536 | 1 | 0.35 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haidi Zhu | 1 | 1 | 0.35 |
| Xin Yan | 2 | 1 | 0.35 |
| Hongying Tang | 3 | 1 | 0.35 |
| Yuchao Chang | 4 | 1 | 0.35 |
| Baoqing Li | 5 | 114 | 20.13 |
| Xiaobing Yuan | 6 | 4 | 1.74 |