Name
Abstract | ||
|---|---|---|
Counting (identical) objects in images is a simple yet fundamental recognition task that requires exhaustive human effort. Automation of this task would reduce the human load significantly. In this paper, we propose a statistical method to automatically count objects in an image sequence by using Higher-order Local Auto-Correlation (HLAC) based image features and Multiple Regression Analysis (MRA). This method is based on a simple computation, which enables fast and automatic object counting in real time. We propose several methods that have different preprocessing and image features and conduct comparative experiments of counting objects (ducks in this paper) in images captured by outdoor monitoring cameras. The experimental results demonstrated the effectiveness of the proposed methods. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/BLISS.2008.21 | BLISS |
Keywords | Field | DocType |
fundamental recognition task,exhaustive human effort,multiple regression analysis,image sequence,image feature,simple computation,higher-order local auto-correlation,human load,automatic object,hlac approach,statistical method,color,regression analysis,pixel,image features,higher order,real time,feature extraction | Object detection,Computer vision,Pattern recognition,Regression analysis,Feature (computer vision),Feature extraction,Automation,Preprocessor,Pixel,Artificial intelligence,Mathematics,Computation | Conference |
Citations | PageRank | References |
5 | 0.65 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takumi Kobayashi | 1 | 241 | 31.18 |
| Tadaaki Hosaka | 2 | 22 | 6.64 |
| Shu Mimura | 3 | 7 | 1.40 |
| Takashi Hayashi | 4 | 5 | 0.65 |
| Nobuyuki Otsu | 5 | 654 | 82.30 |