Name
Abstract | ||
|---|---|---|
In this paper, we presents a background subtraction approach with superpixel and k-means that aims to use less memory to establish a background model and less computation time for moving object detection. We use superpixels to divide similar pixels into the same area, K-mean is used to obtain the main color values of the superpixel. The mean and variance of superpixels and changes in the number of previous attractions are used as the discriminative features. The main contribution of this paper is to propose features suitable for superpixel-based moving object detection. We test this method in different videos demonstrate that this method demonstrated equal or better segmentation than the other techniques and proved capable of processing 320 * 240 video at 114 fps, including post-processing, faster than most existing algorithms. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-95933-7_14 | INTELLIGENT COMPUTING THEORIES AND APPLICATION, PT II |
Keywords | Field | DocType |
Background subtraction, Superpixel, K-Means | Background subtraction,k-means clustering,Object detection,Pattern recognition,Computer science,Segmentation,Pixel,Artificial intelligence,Discriminative model,Computation | Conference |
Volume | ISSN | Citations |
10955 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Qiu Chen | 1 | 2 | 0.69 |
| Zhan-Li Sun | 2 | 4 | 1.76 |
| Nan Wang | 3 | 93 | 27.47 |
| Xin-Yuan Bao | 4 | 0 | 0.68 |