Open AccessEfficient foreground detection for real‐time surveillance applications
Author(s) -
Gruenwedel S.,
Petrović N.I.,
Jovanov L.,
NiñoCastañeda J.O.,
Pižurica A.,
Philips W.
Publication year - 2013
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2013.1944
Subject(s) - background subtraction , foreground detection , computer science , block (permutation group theory) , artificial intelligence , computer vision , object detection , term (time) , adaptation (eye) , filter (signal processing) , frame (networking) , novelty detection , motion detection , pattern recognition (psychology) , pixel , novelty , motion (physics) , mathematics , telecommunications , philosophy , physics , geometry , theology , quantum mechanics , optics
The problem of foreground detection in real‐time video surveillance applications is addressed. Proposes is a framework, which is computationally cheap and has low memory requirements. It combines two simple processing blocks, both of which are essentially background subtraction algorithms. The main novelty of the approach is a combination of an autoregressive moving average filter with two background models having different adaptation speeds. The first model, having a lower adaptation speed, models long‐term background and detects foreground objects by finding areas in the current frame which significantly differ from the proposed background model. The second model, with a higher adaptation speed, models the short‐term background and is responsible for finding regions in the scene with a high foreground object activity. The final foreground detection is built by combining the outputs from these building blocks. The foreground obtained by the long‐term modelling block is verified by the output of the short‐term modelling block, i.e. only the objects exhibiting significant motion are detected as real foreground objects. The proposed method results in a very good foreground detection performance at a low computational cost.