Name
Abstract | ||
|---|---|---|
This paper deals with the selection of relevant motion from multi-object movement. The proposed method is based on a multi-scale approach using features extracted from optical flow and global rarity quantification to compute bottom-up saliency maps. It shows good results from four objects to dense crowds with increasing performance. The results are convincing on synthetic videos, simple real video movements, a pedestrian database and they seem promising on very complex videos with dense crowds. This algorithm only uses motion features (direction and speed) but can be easily generalized to other dynamic or static features. Video surveillance, social signal processing and, in general, higher level scene understanding can benefit from this method. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICIP.2011.6116099 | 2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
Keywords | Field | DocType |
crowd analysis, social signal processing, saliency, attention, real life, real-time | Crowds,Signal processing,Computer vision,Pattern recognition,Salience (neuroscience),Computer science,Top-down and bottom-up design,Feature extraction,Artificial intelligence,Motion estimation,Optical flow | Conference |
ISSN | Citations | PageRank |
1522-4880 | 27 | 0.93 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matei Mancas | 1 | 315 | 27.50 |
| Nicolas Riche | 2 | 184 | 9.75 |
| Julien Leroy | 3 | 53 | 10.28 |
| Bernard Gosselin | 4 | 198 | 12.88 |