Sciweavers

Share
PAMI
2008

Learning to Detect Moving Shadows in Dynamic Environments

11 years 1 months ago
Learning to Detect Moving Shadows in Dynamic Environments
We propose a novel adaptive technique for detecting moving shadows and distinguishing them from moving objects in video sequences. Most methods for detecting shadows work in a static setting with significant human input. To remove these limitations, we propose a more general semisupervised learning technique to tackle the problem. First, we exploit characteristic differences in color and edges in the video frames to come up with a set of features useful for classification. Second, we use a learning technique that employs Support Vector Machines and the co-training algorithm, which relies on a small set of humanlabeled data. We observe a surprising phenomenon that co-training can counter the effects of changing underlying probability distributions in the input space. From the standpoint of detecting shadows, once deployed, the proposed method can dynamically adapt to varying conditions without any manual intervention and performs better classification than previous methods on static and...
Ajay J. Joshi, Nikolaos Papanikolopoulos
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2008
Where PAMI
Authors Ajay J. Joshi, Nikolaos Papanikolopoulos
Comments (0)
books