Name
Abstract | ||
|---|---|---|
A novel method for detecting the average speed of traffic from non-stationary aerial video is presented. The method first extracts interest points from a pair of frames and performs interest point tracking with an optical flow algorithm. The output of the optical flow is a set of motion vectors which are k-means clustered in velocity space. The centers of the clusters correspond to the average velocities of traffic and the background, and are used to determine the speed of traffic relative to the background. The proposed method is tested on a 70-frame test sequence of UAV aerial video, and achieves an average error for speed estimates of less than 12%. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ISC2.2015.7366230 | 2015 IEEE First International Smart Cities Conference (ISC2) |
Keywords | DocType | Citations |
Traffic speed detection,unmanned aerial vehicle (UAV),optical flow,interest point tracking,k-means clustering | Conference | 4 |
PageRank | References | Authors |
0.44 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ke Ruimin | 1 | 89 | 6.69 |
| Sung Kim | 2 | 72 | 7.41 |
| zhibin li | 3 | 29 | 2.92 |
| Yinhai Wang | 4 | 292 | 39.37 |