Journal ArticleDOI
Unsupervised Moving Object Detection in Complex Scenes Using Adversarial Regularizations
TLDR
A Generative Adversarial Network (GAN) based on a moving object detection algorithm, called MOD_GAN, is proposed, enabling the algorithm to learn generating background sequences using input from uniformly distributed random noise samples.Abstract:
Moving object detection (MOD) is a fundamental step in many high-level vision-based applications, such as human activity analysis, visual object tracking, autonomous vehicles, surveillance, and security. Most of the existing MOD algorithms observe performance degradation in the presence of complex scenes containing camouflage objects, shadows, dynamic backgrounds, and varying illumination conditions, and captured by static cameras. To appropriately handle these challenges, we propose a Generative Adversarial Network (GAN) based on a moving object detection algorithm, called MOD_GAN. In the proposed algorithm, scene-specific GANs are trained in an unsupervised MOD setting, thereby enabling the algorithm to learn generating background sequences using input from uniformly distributed random noise samples. In addition to adversarial loss, during training, norm-based loss in the image space and discriminator feature-space is also minimized between the generated images and the training data. The additional losses enable the generator to learn subtle background details, resulting in a more realistic complex scene generation. During testing, a novel back-propagation based algorithm is used to generate images with statistics similar to the test images. More appropriate random noise samples are searched by directly minimizing the loss function between the test and generated images both in the image and discriminator feature-spaces. The network is not updated in this step; only the input noise samples are iteratively modified to minimize the loss function. Moreover, motion information is used to ensure that this loss is only computed on small-motion pixels. A novel dataset containing outdoor time-lapsed images from dawn to dusk with a full illumination variation cycle is also proposed to better compare the MOD algorithms in outdoor scenes. Accordingly, extensive experiments on five benchmark datasets and comparison with 30 existing methods demonstrate the strength of the proposed algorithm.read more
Citations
More filters
Journal Article
“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告
Journal ArticleDOI
U2-ONet: A Two-Level Nested Octave U-Structure Network with a Multi-Scale Attention Mechanism for Moving Object Segmentation
TL;DR: U2-ONet as mentioned in this paper proposes a two-level nested octave U-structure network with a multi-scale attention mechanism, which takes two RGB frames, the optical flow between these frames, and the instance segmentation of the frames as inputs.
Journal ArticleDOI
Moving Human Target Detection and Tracking in Video Frames
TL;DR: The proposed method has shown outperforming results for various performance parameters such as precision, accuracy, recall, and the F1-score under three different lighting conditions and shows a reduction in time complexity in comparison with the state-of-the-art algorithms.
Journal ArticleDOI
Deep Learning-based Moving Object Segmentation: Recent Progress and Research Prospects
TL;DR: In this article , the authors present a more up-to-date categorization based on model characteristics, then compare and discuss each category from feature learning (FL), and model training and evaluation perspectives.
Journal ArticleDOI
Unsupervised moving object segmentation using background subtraction and optimal adversarial noise sample search
TL;DR: In this article , the authors proposed a MOS algorithm exploiting multiple adversarial regularizations including conventional as well as least squares losses, which force the generator to synthesize dynamic background similar to the test sequences which upon subtraction results in moving objects segmentation.
References
More filters
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Proceedings ArticleDOI
Image-to-Image Translation with Conditional Adversarial Networks
TL;DR: Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
Proceedings ArticleDOI
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
TL;DR: CycleGAN as discussed by the authors learns a mapping G : X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss.
Posted Content
Image-to-Image Translation with Conditional Adversarial Networks
TL;DR: Conditional Adversarial Network (CA) as discussed by the authors is a general-purpose solution to image-to-image translation problems, which can be used to synthesize photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
Proceedings ArticleDOI
Adaptive background mixture models for real-time tracking
Chris Stauffer,W.E.L. Grimson +1 more
TL;DR: This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model, resulting in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes.