Name
Abstract | ||
|---|---|---|
Motion detection, the process which segments moving objects in video streams, is the first critical process and plays an important role in video surveillance systems. Dynamic scenes are commonly encountered in both indoor and outdoor situations and contain objects such as swaying trees, spouting fountains, rippling water, moving curtains, and so on. However, complete and accurate motion detection in dynamic scenes is often a challenging task. This paper presents a novel motion detection approach based on radial basis function artificial neural networks to accurately detect moving objects not only in dynamic scenes but also in static scenes. The proposed method involves two important modules: a multibackground generation module and a moving object detection module. The multibackground generation module effectively generates a flexible probabilistic model through an unsupervised learning process to fulfill the property of either dynamic background or static background. Next, the moving object detection module achieves complete and accurate detection of moving objects by only processing blocks that are highly likely to contain moving objects. This is accomplished by two procedures: the block alarm procedure and the object extraction procedure. The detection results of our method were evaluated by qualitative and quantitative comparisons with other state-of-the-art methods based on a wide range of natural video sequences. The overall results show that the proposed method substantially outperforms existing methods with Similarity and F₁ accuracy rates of 69.37% and 65.50%, respectively. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TCYB.2013.2248057 | IEEE Transactions on Systems, Man, and Cybernetics |
Keywords | Field | DocType |
video streams,neural network,video signal processing,spouting fountains,radial basis function networks,dynamic background,radial basis function based neural network,flexible probabilistic model,swaying trees,motion detection,radial basis function artificial neural networks,moving object detection module,static scenes,dynamic scenes,moving objects,multibackground generation module,object extraction procedure,video surveillance systems,natural video sequences,static background,unsupervised learning process,rippling water,block alarm procedure,video surveillance | Computer vision,Object detection,Radial basis function,Motion detection,Computer science,Unsupervised learning,Vehicle dynamics,Artificial intelligence,Statistical model,Probabilistic logic,Artificial neural network,Machine learning | Journal |
Volume | Issue | ISSN |
44 | 1 | 2168-2267 |
Citations | PageRank | References |
23 | 0.67 | 38 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shih-Chia Huang | 1 | 657 | 42.31 |
| Ben-Hsiang Do | 2 | 30 | 1.12 |