scholarly journals Fast and Accurate Background Reconstruction Using Background Bootstrapping

2021 ◽  
Author(s):  
Bruno Sauvalle ◽  
Arnaud de La Fortelle
Keyword(s):  
Gradient Descent ◽  
Moving Objects ◽  
Stochastic Gradient Descent ◽  
Current Estimate ◽  
Background Estimation ◽  
Illumination Changes ◽  
Background Reconstruction ◽  
Image Pixels ◽  
Definition Of ◽  
Background Image

The goal of background reconstruction is to recover the background image of a scene from a sequence of frames showing this scene cluttered by various moving objects. This task is fundamental in image analysis, and is generally the first step before more advanced processing, but difficult because there is no formal definition of what should be considered as background or foreground and the results may be severely impacted by various challenges such as illumination changes, intermittent object motions, highly cluttered scenes, etc. We propose in this paper a new iterative algorithm for background reconstruction, where the current estimate of the background is used to guess which image pixels are background pixels and a new background estimation is performed using those pixels only. We then show that the proposed algorithm, which uses stochastic gradient descent for improved regularization, is more accurate than the state of the art on the challenging SBMnet dataset, especially for short videos with low frame rates, and is also fast, reaching an average of 52 fps on this dataset when parameterized for maximal accuracy using GPU acceleration and a Python implementation.

scholarly journals Fast and Accurate Background Reconstruction Using Background Bootstrapping

2022 ◽  
Vol 8 (1) ◽  
pp. 9
Author(s):  
Bruno Sauvalle ◽  
Arnaud de La Fortelle
Keyword(s):  
Moving Objects ◽  
Graphics Processing Unit ◽  
Stochastic Gradient Descent ◽  
Processing Unit ◽  
Current Estimate ◽  
Background Estimation ◽  
Background Reconstruction ◽  
Image Pixels ◽  
Definition Of ◽  
Graphics Processing

The goal of background reconstruction is to recover the background image of a scene from a sequence of frames showing this scene cluttered by various moving objects. This task is fundamental in image analysis, and is generally the first step before more advanced processing, but difficult because there is no formal definition of what should be considered as background or foreground and the results may be severely impacted by various challenges such as illumination changes, intermittent object motions, highly cluttered scenes, etc. We propose in this paper a new iterative algorithm for background reconstruction, where the current estimate of the background is used to guess which image pixels are background pixels and a new background estimation is performed using those pixels only. We then show that the proposed algorithm, which uses stochastic gradient descent for improved regularization, is more accurate than the state of the art on the challenging SBMnet dataset, especially for short videos with low frame rates, and is also fast, reaching an average of 52 fps on this dataset when parameterized for maximal accuracy using acceleration with a graphics processing unit (GPU) and a Python implementation.

Adaptive Background Image Calculation for Video Sequences Based on Optical Flow

2014 ◽  
Vol 945-949 ◽  
pp. 1820-1824
Author(s):  
Hui Zhu ◽  
Xiao Peng Ji
Keyword(s):  
Optical Flow ◽  
Video Processing ◽  
Moving Objects ◽  
Averaging Method ◽  
Moving Object ◽  
New Method ◽  
Video Sequences ◽  
Background Estimation ◽  
Traffic Video ◽  
Background Image

A new method is proposed to calculate the background in video sequences. The optical flow is estimated to determine the local regions occupied by moving objects. The background image is calculated by an efficient averaging process excluding the moving object regions, which overcomes the foreground-occluding problem in direct averaging method for background estimation. The experiments for traffic video processing prove the method’s effectiveness and robustness.

scholarly journals Moving Objects Semantic Segmentation using SegNet with VGG Encoder for Autonomous Driving

2021 ◽  
Author(s):  
Wahyudi Setiawan ◽  
Kori Cahyono
Keyword(s):  
Neural Network ◽  
Network Optimization ◽  
Gradient Descent ◽  
Moving Objects ◽  
Object Segmentation ◽  
Image Data ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Stochastic Gradient Descent ◽  
Road Pavement

Segmentation and recognition become the general steps to identify objects. This research discusses pixel-wise semantic segmentation based on moving objects. The data from the CamVid video which is a collection of autonomous driving images. The image data consist of 701 images accompanied by labels. The segmentation and recognition of 11 objects contained in the image (sky, building, pole, road, pavement, tree, sign-symbol, fence, car, pedestrian and bicyclist) is representing. This moving object segmentation is carried out using SegNet which is one of the Convolutional Neural Network (CNN) methods. Image segmentation on CNN generally consists of two parts: Encoder and Decoder. VGG16 and VGG19 pre-trained networks are used as encoders, while decoders are the upsampling of encoders. Network optimization uses stochastic gradient descent of Momentum (SGDM). The test produces the best recognition was road objects with an accuracy of 0.96013, IoU 0.93745, F1-Score 0.8535 using VGG19 encoder, while when using VGG16 encoder accuracy was 0.94162, IoU 0.92309, and F1-Score 0.8535.

scholarly journals BACKGROUND ESTIMATION IN KERNEL SPACE

2011 ◽  
Vol 25 (01) ◽  
pp. 1-35 ◽  
Author(s):  
HAMIDREZA BARADARAN KASHANI ◽  
HADI SADOGHI YAZDI ◽  
SEYED ALIREZA SEYEDIN
Keyword(s):  
Function Approximation ◽  
Moving Objects ◽  
Learning Algorithm ◽  
Estimation Method ◽  
Least Mean Square ◽  
Background Estimation ◽  
Illumination Changes ◽  
Weighted Kernel ◽  
Kernel Least Mean Square ◽  
Two Stages

One problem in background estimation is the inherent change in the background such as waving tree branches, water surfaces, camera shakes, and the existence of moving objects in every image. In this paper, a new method for background estimation is proposed based on function approximation in kernel domain. For this purpose, Weighted Kernel-based Learning Algorithm (WKLA) is designed. WKLA includes a weighted type of kernel least mean square algorithm with ability to function approximation in the presence of noise. So, the proposed background estimation method includes two stages: firstly, a novel algorithm for outlier detection namely Fuzzy Outlier Detector (FOD) is applied. Then obtained results are fed to the WKLA. The proposed approach can handle scenes containing moving backgrounds, gradual illumination changes, camera vibrations, and non-empty backgrounds. The qualitative results and quantitative evaluations on various indoor and outdoor sequences relative to existing approaches show the high accuracy and effectiveness of the proposed method in background estimation and foreground detection.

Scene Configurator

2012 ◽  
Vol 17 (4) ◽  
pp. 217-222
Author(s):  
Piotr Szymczyk ◽  
Magdalena Szymczyk
Keyword(s):  
Light Source ◽  
Protected Area ◽  
Moving Objects ◽  
Video System ◽  
Definition Of ◽  
The Guardian

Abstract In this paper authors describe in details a system dedicated to scene configuration. The user can define different important 2D regions of the scene. There is a possibility to define the following kinds of regions: flour, total covering, down covering, up covering, middle covering, entrance/exit, protected area, prohibited area, allowed direction, prohibited direction, reflections, moving objects, light source, wall and sky. The definition of this regions is very important to further analysis of live stream camera data in the guardian video system.

Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

2020 ◽  
Vol 4 (2) ◽  
pp. 329-335
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Purwono
Keyword(s):  
Social Media ◽  
Gradient Descent ◽  
Classification Model ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Performance Patterns ◽  
A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Gradient Descent ◽  
Autonomous Driving ◽  
Driving Behavior ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Safe Driving ◽  
Processing Pipeline ◽  
Self Driving Cars ◽  
And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

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