Name
Title | ||
|---|---|---|
Extended Co-Occurrence Hog With Dense Trajectories For Fine-Grained Activity Recognition |
Abstract | ||
|---|---|---|
In this paper we propose a novel feature descriptor Extended Co-occurrence HOG (ECoHOG) and integrate it with dense point trajectories demonstrating its usefulness in fine grained activity recognition. This feature is inspired by original Co-occurrence HOG (CoHOG) that is based on histograms of occurrences of pairs of image gradients in the image. Instead relying only on pure histograms we introduce a sum of gradient magnitudes of co-occurring pairs of image gradients in the image. This results in giving the importance to the object boundaries and straightening the difference between the moving foreground and static background. We also couple ECoHOG with dense point trajectories extracted using optical flow from video sequences and demonstrate that they are extremely well suited for fine grained activity recognition. Using our feature we outperform state of the art methods in this task and provide extensive quantitative evaluation. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-16814-2_22 | COMPUTER VISION - ACCV 2014, PT V |
Field | DocType | Volume |
Computer vision,Histogram,Feature descriptor,Activity recognition,Pattern recognition,Computer science,Co-occurrence,Artificial intelligence,Optical flow | Conference | 9007 |
ISSN | Citations | PageRank |
0302-9743 | 4 | 0.43 |
References | Authors | |
13 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hirokatsu Kataoka | 1 | 31 | 18.41 |
| Kiyoshi Hashimoto | 2 | 16 | 1.51 |
| Kenji Iwata | 3 | 14 | 6.52 |
| Yutaka Satoh | 4 | 81 | 19.19 |
| Nassir Navab | 5 | 6594 | 578.60 |
| Slobodan Ilic | 6 | 1307 | 67.56 |
| Yoshimitsu Aoki | 7 | 80 | 23.65 |