.png){kind=icon}
.png){kind=icon}
Return
A keypoint-based method for background modeling and foreground detection using a PTZ camera
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2016.10.015.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The automatic scene analysis is still a topic of great interest in computer vision due to the growing possibilities provided by the increasingly sophisticated optical cameras. The background modeling, including its initialization and its updating, is a crucial aspect that can play a main role in a wide range of application domains, such as vehicle tracking, person re-identification and object recognition. In any case, many challenges still remain partially unsolved, including camera movements (i.e., pan/tilt), scale changes (i.e., zoom-in/zoom-out) and deletion of the initial foreground elements from the background model. This paper describes a method for background modeling and foreground detection able to address all the mentioned challenges. In particular, the proposed method uses a spatio-temporal tracking of sets of keypoints to distinguish the background from the foreground. It analyses these sets by a grid strategy to estimate both camera movements and scale changes. The same sets are also used to construct a panoramic background model and to delete the possible initial foreground elements from it. Experiments carried out on some challenging videos from three different datasets (i.e., PBI, VOT and Airport MotionSeg) demonstrate the effectiveness of the method on PTZ cameras. Other videos from a further dataset (i.e., FBMS) have been used to measure the accuracy of the proposed method with respect to some key works of the current state-of-the-art. Finally, some videos from another dataset (i.e., SBI) have been used to test the method on stationary cameras. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Background modeling
Foreground detection
PTZ cameras
Grid strategy
Spatio-temporal tracking of keypoints
Background estimation
Background updating
Background reconstruction
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
7.9K
Citations:
1.6W