25 research outputs found
CVABS: Moving Object Segmentation with Common Vector Approach for Videos
Background modelling is a fundamental step for several real-time computer
vision applications that requires security systems and monitoring. An accurate
background model helps detecting activity of moving objects in the video. In
this work, we have developed a new subspace based background modelling
algorithm using the concept of Common Vector Approach with Gram-Schmidt
orthogonalization. Once the background model that involves the common
characteristic of different views corresponding to the same scene is acquired,
a smart foreground detection and background updating procedure is applied based
on dynamic control parameters. A variety of experiments is conducted on
different problem types related to dynamic backgrounds. Several types of
metrics are utilized as objective measures and the obtained visual results are
judged subjectively. It was observed that the proposed method stands
successfully for all problem types reported on CDNet2014 dataset by updating
the background frames with a self-learning feedback mechanism.Comment: 12 Pages, 4 Figures, 1 Tabl
DeepPBM: Deep Probabilistic Background Model Estimation from Video Sequences
This paper presents a novel unsupervised probabilistic model estimation of
visual background in video sequences using a variational autoencoder framework.
Due to the redundant nature of the backgrounds in surveillance videos, visual
information of the background can be compressed into a low-dimensional subspace
in the encoder part of the variational autoencoder, while the highly variant
information of its moving foreground gets filtered throughout its
encoding-decoding process. Our deep probabilistic background model (DeepPBM)
estimation approach is enabled by the power of deep neural networks in learning
compressed representations of video frames and reconstructing them back to the
original domain. We evaluated the performance of our DeepPBM in background
subtraction on 9 surveillance videos from the background model challenge
(BMC2012) dataset, and compared that with a standard subspace learning
technique, robust principle component analysis (RPCA), which similarly
estimates a deterministic low dimensional representation of the background in
videos and is widely used for this application. Our method outperforms RPCA on
BMC2012 dataset with 23% in average in F-measure score, emphasizing that
background subtraction using the trained model can be done in more than 10
times faster
Real-Time Semantic Background Subtraction
Semantic background subtraction SBS has been shown to improve the performance
of most background subtraction algorithms by combining them with semantic
information, derived from a semantic segmentation network. However, SBS
requires high-quality semantic segmentation masks for all frames, which are
slow to compute. In addition, most state-of-the-art background subtraction
algorithms are not real-time, which makes them unsuitable for real-world
applications. In this paper, we present a novel background subtraction
algorithm called Real-Time Semantic Background Subtraction (denoted RT-SBS)
which extends SBS for real-time constrained applications while keeping similar
performances. RT-SBS effectively combines a real-time background subtraction
algorithm with high-quality semantic information which can be provided at a
slower pace, independently for each pixel. We show that RT-SBS coupled with
ViBe sets a new state of the art for real-time background subtraction
algorithms and even competes with the non real-time state-of-the-art ones. Note
that we provide python CPU and GPU implementations of RT-SBS at
https://github.com/cioppaanthony/rt-sbs.Comment: Accepted and Published at ICIP 202
Dynamic tree-structured sparse RPCA via column subset selection for background modeling and foreground detection
Video analysis often begins with background subtraction, which consists of creation of a background model that allows distinguishing foreground pixels. Recent evaluation of background subtraction techniques demonstrated that there are still considerable challenges facing these methods. Processing per-pixel basis from the background is not only time-consuming but also can dramatically affect foreground region detection, if region cohesion and contiguity is not considered in the model. We present a new method in which we regard the image sequence to be made up of the sum of a low-rank background matrix and a dynamic tree-structured sparse matrix, and solve the decomposition using our approximated Robust Principal Component Analysis method extended to handle camera motion. Furthermore, to reduce the curse of dimensionality and scale, we introduce a low-rank background modeling via Column Subset Selection that reduces the order of complexity, decreases computation time, and eliminates the huge storage need for large videos
New Students' Self-Adjustment at Ar-Risalah Islamic Junior High School: Roles and Supporting Factors
The purpose of this research is to determine teachers’ roles in improving students' self-adjustment and its supporting factors at Ar-Risalah Islamic Junior High School, Padang, Indonesia. This is qualitative descriptive research with data collected from teachers, students, principals, and other school officials through interview, observation, and documentation. The data collected were analyzed using reduction and triangulation processes. The result showed that the teacher had expertise as a motivator to support and trigger the student enthusiasm in adapting and learning new materials at school. Meanwhile, the inhibiting factors of the self-adjustment process were parents that have not been able to let their children go to boarding schools and teachers without psychological backgrounds, because this background are needed for better approach and intervention for children to make them enthusiasm in learning in boarding schoo