Name
Abstract | ||
|---|---|---|
This paper presents a novel tool for detecting human actions in stationary surveillance camera videos. In the proposed method there is no need to detect and track the human body or to detect the spatial or spatio-temporal interest points of the events. Instead our method computes single-scale spatio-temporal descriptors to characterize the action patterns. Two different descriptors are evaluated: histograms of optical flow directions and histograms of frame difference gradients. The integral video method is also presented to improve the performance of the extraction of these features. We evaluated our methods on two datasets: a public dataset containing actions of persons drinking and a new dataset containing stand up events. According to our experiments both detectors are suitable for indoor applications and provide a robust tool for practical problems such as moving background, or partial occlusion. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-17691-3_34 | ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, PT II |
Keywords | Field | DocType |
Human action recognition,optical flow,frame difference | Computer vision,Histogram,Pattern recognition,Computer science,Frame difference,Surveillance camera,Artificial intelligence,Optical flow | Conference |
Volume | ISSN | Citations |
6475 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 16 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ákos Utasi | 1 | 49 | 6.40 |
| Andrea Kovács | 2 | 37 | 3.56 |