11,923 research outputs found
On the evaluation of background subtraction algorithms without ground-truth
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. C. San Miguel, and J. M. MartĂnez, "On the evaluation of background subtraction algorithms without ground-truth" in 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2013, 180 - 187In video-surveillance systems, the moving object segmentation stage (commonly based on background subtraction) has to deal with several issues like noise, shadows and multimodal backgrounds. Hence, its failure is inevitable and its automatic evaluation is a desirable requirement for online analysis. In this paper, we propose a hierarchy of existing performance measures not-based on ground-truth for video object segmentation. Then, four measures based on color and motion are selected and examined in detail with different segmentation algorithms and standard test sequences for video object segmentation. Experimental results show that color-based measures perform better than motion-based measures and background multimodality heavily reduces the accuracy of all obtained evaluation results.This work is partially supported by the Spanish
Government (TEC2007- 65400 SemanticVideo), by
CĂĄtedra Infoglobal-UAM for âNuevas TecnologĂas de
video aplicadas a la seguridadâ, by the ConsejerĂa de
EducaciĂłn of the Comunidad de Madrid and by the
European Social Fund
BSUV-Net: a fully-convolutional neural network for background subtraction of unseen videos
Background subtraction is a basic task in computer vision and video processing often applied as a pre-processing step for object tracking, people recognition, etc. Recently, a number of successful background-subtraction algorithms have been proposed, however nearly all of the top-performing ones are supervised. Crucially, their success relies upon the availability of some annotated frames of the test video during training. Consequently, their performance on completely âunseenâ videos is undocumented in the literature. In this work, we propose a new, supervised, background subtraction algorithm for unseen videos (BSUV-Net) based on a fully-convolutional neural network. The input to our network consists of the current frame and two background frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance of overfitting, we also introduce a new data-augmentation technique which mitigates the impact of illumination difference between the background frames and the current frame. On the CDNet-2014 dataset, BSUV-Net outperforms stateof-the-art algorithms evaluated on unseen videos in terms of several metrics including F-measure, recall and precision.Accepted manuscrip
A fully-convolutional neural network for background subtraction of unseen videos
Background subtraction is a basic task in computer vision
and video processing often applied as a pre-processing step
for object tracking, people recognition, etc. Recently, a number of successful background-subtraction algorithms have
been proposed, however nearly all of the top-performing
ones are supervised. Crucially, their success relies upon
the availability of some annotated frames of the test video
during training. Consequently, their performance on completely âunseenâ videos is undocumented in the literature.
In this work, we propose a new, supervised, backgroundsubtraction algorithm for unseen videos (BSUV-Net) based
on a fully-convolutional neural network. The input to our
network consists of the current frame and two background
frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance
of overfitting, we also introduce a new data-augmentation
technique which mitigates the impact of illumination difference between the background frames and the current frame.
On the CDNet-2014 dataset, BSUV-Net outperforms stateof-the-art algorithms evaluated on unseen videos in terms of
several metrics including F-measure, recall and precision.Accepted manuscrip
Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
People detection in single 2D images has improved greatly in recent years.
However, comparatively little of this progress has percolated into multi-camera
multi-people tracking algorithms, whose performance still degrades severely
when scenes become very crowded. In this work, we introduce a new architecture
that combines Convolutional Neural Nets and Conditional Random Fields to
explicitly model those ambiguities. One of its key ingredients are high-order
CRF terms that model potential occlusions and give our approach its robustness
even when many people are present. Our model is trained end-to-end and we show
that it outperforms several state-of-art algorithms on challenging scenes
An FPGA-based infant monitoring system
We have designed an automated visual surveillance system for monitoring sleeping infants. The low-level image
processing is implemented on an embedded Xilinxâs Virtex
II XC2v6000 FPGA and quantifies the level of scene activity using a specially designed background subtraction algorithm. We present our algorithm and show how we have
optimised it for this platform
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