Name
Abstract | ||
|---|---|---|
This paper presents a method for detecting people based on the co-occurrence of appearance and spatiotemporal features. Histograms of oriented gradients(HOG) are used as appearance features, and the results of pixel state analysis are used as spatiotemporal features. The pixel state analysis classifies foreground pixels as either stationary or transient. The appearance and spatiotemporal features are projected into subspaces in order to reduce the dimensions of the vectors by principal component analysis(PCA). The cascade AdaBoost classifier is used to represent the co-occurrence of the appearance and spatiotemporal features. The use of feature co-occurrence, which captures the similarity of appearance, motion, and spatial information within the people class, makes it an effective detector. Experimental results show that the performance of our method is about 29% better than that of the conventional method. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICPR.2008.4761809 | ICPR |
Keywords | Field | DocType |
adaboost,cascade adaboost classifier,image classification,object detection,pixel state analysis,histograms of oriented gradients,spatiotemporal features,people detection,co-occurrence,principal component analysis,feature extraction,spatial information,histograms,pixel | Spatial analysis,Histogram,Object detection,Computer vision,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Pixel,Contextual image classification,Detector,Principal component analysis | Conference |
ISSN | ISBN | Citations |
1051-4651 E-ISBN : 978-1-4244-2175-6 | 978-1-4244-2175-6 | 11 |
PageRank | References | Authors |
0.62 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuji Yamauchi | 1 | 43 | 10.45 |
| fujiyoshi | 2 | 730 | 101.43 |
| Bon-Woo Hwang | 3 | 177 | 16.33 |
| Takeo Kanade | 4 | 25073 | 4203.02 |