An Optimization Algorithm of Foreground Objects Extraction

2011 ◽  
Vol 403-408 ◽  
pp. 169-176
Author(s):  
Xia Yi Zhang ◽  
Zhi Peng Li ◽  
Fu Qiang Liu ◽  
Zhen Jia ◽  
Jian Wei Zhao
Keyword(s):  
Background Subtraction ◽  
Gaussian Mixture Models ◽  
Computational Cost ◽  
Gaussian Mixture ◽  
Object Extraction ◽  
Detection Accuracy ◽  
Foreground Object ◽  
Image Matting ◽  
New Approach ◽  
Novel Approach

In this paper, we propose a novel algorithm for coarse-to-fine foreground objects extraction. There are two general approaches for foreground objects extraction: background subtraction and image matting. Our new approach can not only improve detection accuracy compared with general background subtraction approaches, but also reduce computation burden compared with general image matting approaches. Firstly, we present a novel method called Motion-mask Gaussian Mixture Models (Motion-mask GMMs) to extract coarse foreground regions. This new approach can classify foreground and background pixels more accurately, especially when there are long-time stopping objects in the scene. Secondly, with the coarse foreground regions, we propose a novel approach to make foreground object extraction more accurate based on effective fusion of image registration and image matting. This new method overcomes the template drift problem during template updating and also reduces the expensive computational cost of image matting. Our proposed approach is tested with kinds of video sequences in indoor and outdoor environments. Experimental results demonstrate the accuracy and efficiency of our proposed approach for foreground object extraction.

Multi Object Detection and Tracking from Video File

2014 ◽  
Vol 533 ◽  
pp. 218-225 ◽  
Author(s):  
Rapee Krerngkamjornkit ◽  
Milan Simic
Keyword(s):  
Computer Vision ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Video Tracking ◽  
Partial Occlusion ◽  
Video File ◽  
Detection And Tracking ◽  
Over Time

This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results show that system can operate in real time and successfully detect, track and identify multiple targets in the presence of partial occlusion.

scholarly journals Deep-Learning-Based Road Crack Detection Frameworks for Dashcam-captured Images under Different Illumination Conditions

2021 ◽  
Author(s):  
Da-Ren Chen ◽  
Wei-Min Chiu
Keyword(s):  
Object Detection ◽  
Large Scale ◽  
Crack Detection ◽  
State Of The Art ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
The Road ◽  
Art Object

Abstract Machine learning techniques have been used to increase detection accuracy of cracks in road surfaces. Most studies failed to consider variable illumination conditions on the target of interest (ToI), and only focus on detecting the presence or absence of road cracks. This paper proposes a new road crack detection method, IlumiCrack, which integrates Gaussian mixture models (GMM) and object detection CNN models. This work provides the following contributions: 1) For the first time, a large-scale road crack image dataset with a range of illumination conditions (e.g., day and night) is prepared using a dashcam. 2) Based on GMM, experimental evaluations on 2 to 4 levels of brightness are conducted for optimal classification. 3) the IlumiCrack framework is used to integrate state-of-the-art object detecting methods with CNN to classify the road crack images into eight types with high accuracy. Experimental results show that IlumiCrack outperforms the state-of-the-art R-CNN object detection frameworks.

scholarly journals Evaluation of speaker de-identification based on voice gender and age conversion

2018 ◽  
Vol 69 (2) ◽  
pp. 138-147 ◽  
Author(s):  
Jiří Přibil ◽  
Anna Přibilová ◽  
Jindřich Matoušek
Keyword(s):  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Scale Transformation ◽  
English Speakers ◽  
Detection Accuracy ◽  
Frequency Scale ◽  
Open Set ◽  
Gender And Age ◽  
Prosody Modification ◽  
Apparent Age

AbstractTwo basic tasks are covered in this paper. The first one consists in the design and practical testing of a new method for voice de-identification that changes the apparent age and/or gender of a speaker by multi-segmental frequency scale transformation combined with prosody modification. The second task is aimed at verification of applicability of a classifier based on Gaussian mixture models (GMM) to detect the original Czech and Slovak speakers after applied voice deidentification. The performed experiments confirm functionality of the developed gender and age conversion for all selected types of de-identification which can be objectively evaluated by the GMM-based open-set classifier. The original speaker detection accuracy was compared also for sentences uttered by German and English speakers showing language independence of the proposed method.

scholarly journals Fi-score: a novel approach to characterise protein topology and aid in drug discovery studies

2020 ◽  
Author(s):  
Austė Kanapeckaitė ◽  
Claudia Beaurivage ◽  
Matthew Hancock ◽  
Erik Verschueren
Keyword(s):  
Dihedral Angle ◽  
Target Selection ◽  
Gaussian Mixture Models ◽  
Drug Repurposing ◽  
Gaussian Mixture ◽  
Rational Drug Design ◽  
Drug Candidates ◽  
Novel Approach ◽  
Protein Fingerprinting ◽  
Rational Drug

AbstractTarget evaluation is at the centre of rational drug design and biologics development. In order to successfully engineer antibodies, T-cell receptors or small molecules it is necessary to identify and characterise potential binding or contact sites on therapeutically relevant target proteins. Currently, there are numerous challenges in achieving a better docking precision as well as characterising relevant sites. We devised a first-of-its-kind in silico protein fingerprinting approach based on dihedral angle and B-factor distribution to probe binding sites and sites of structural importance. In addition, we showed that the entire protein regions or individual structural subsets can be profiled using our derived fi-score based on amino acid dihedral angle and B-factor distribution. We further described a method to assess the structural profile and extract information on sites of importance using machine learning Gaussian mixture models. In combination, these biophysical analytical methods could potentially help to classify and systematically analyse not only targets but also drug candidates that bind to specific sites which would greatly improve pre-screening stage, target selection and drug repurposing efforts in finding other matching targets.

Comparative Study of Statistical background Modeling and Subtraction

2017 ◽  
Vol 8 (2) ◽  
pp. 287
Author(s):  
Mourad Moussa ◽  
Maha Hmila ◽  
Ali Douik
Keyword(s):  
Object Detection ◽  
Background Subtraction ◽  
Moving Objects ◽  
Computational Cost ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Human Motion ◽  
Moving Object Detection ◽  
Moving Object ◽  
Dynamic Scenes

Background subtraction methods are widely exploited for moving object detection in videos in many computer vision applications, such as traffic monitoring, human motion capture and video surveillance. The two most distinguishing and challenging aspects of such approaches in this application field are how to build correctly and efficiently the background model and how to prevent the false detection between; (1) moving background pixels and moving objects, (2) shadows pixel and moving objects. In this paper we present a new method for image segmentation using background subtraction. We propose an effective scheme for modelling and updating a background adaptively in dynamic scenes focus on statistical learning. We also introduce a method to detect sudden illumination changes and segment moving objects during these changes. Unlike the traditional color levels provided by RGB sensor aren’t the best choice, for this reason we propose a recursive algorithm that contributes to select very significant color space. Experimental results show significant improvements in moving object detection in dynamic scenes such as waving tree leaves and sudden illumination change, and it has a much lower computational cost compared to Gaussian mixture model.

ANOMALY DETECTION OF EVENTS IN CROWDED ENVIRONMENT AND STUDY OF VARIOUS BACKGROUND SUBTRACTION METHODS

2017 ◽  
Vol 5 (1) ◽  
pp. 32
Author(s):  
Meenal Suryakant Vatsaraj ◽  
Rajan Vishnu Parab ◽  
Prof.D.S Bade
Keyword(s):  
Anomaly Detection ◽  
Random Field ◽  
Markov Random Field ◽  
Background Subtraction ◽  
Computational Cost ◽  
Gaussian Mixture ◽  
Histogram Of Oriented Gradients ◽  
Markov Random ◽  
Wide Range ◽  
Crowded Environment

Anomalous behavior detection and localization in videos of the crowded area that is specific from a dominant pattern are obtained. Appearance and motion information are taken into account to robustly identify different kinds of an anomaly considering a wide range of scenes. Our concept based on histogram of oriented gradients and markov random field easily captures varying dynamic of the crowded environment. Histogram of oriented gradients along with well known markov random field will effectively recognize and characterizes each frame of each scene. Anomaly detection using artificial neural network consist both appearance and motion features which extract within spatio temporal domain of moving pixels that ensures robustness to local noise and thus increases accuracy in detection of a local anomaly with low computational cost. To extract a region of interest we have to subtract background. Background subtraction is done by various methods like Weighted moving mean, Gaussian mixture model, Kernel density estimation.

Autonomous Anomaly Detection System for Crime Monitoring

2019 ◽  
Vol 16 (8) ◽  
pp. 3410-3418
Author(s):  
Muhammed Shuaau ◽  
Ka Fei Thang
Keyword(s):  
Anomaly Detection ◽  
Trajectory Analysis ◽  
Detection System ◽  
False Positive Rate ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Asia Pacific ◽  
Detection Accuracy ◽  
Positive Rate ◽  
Reception Area

Autonomous anomaly detection has attracted significant amount of attention in the past decade due to increased security concerns all around the world. The volume of data reported by surveillance cameras has outrun human capacity and there exists a greater need for anomaly detection systems for crime monitoring. This project proposes a solution to this problem in a reception area context by using trajectory analysis. Trajectory extraction is proposed by using Gaussian Mixture Models and Kalman Filter for data association. Then trajectory analysis is performed on extracted trajectories to detect four different anomalies which are entering staff area, running, loitering and squatting down. The proposed anomaly detection method is tested on datasets recorded at Asia Pacific University’s reception area. The proposed algorithms were able to achieve a detection accuracy of 89% and a false positive rate of 4.52%. The results presented show the effectiveness of the proposed method.

scholarly journals Autonomous Abnormal Behaviour Detection Using Trajectory Analysis

2019 ◽  
Vol 9 (4) ◽  
pp. 2403
Author(s):  
Muhammed Shuaau ◽  
Ka Fei Thang ◽  
Nai Shyan Lai
Keyword(s):  
Anomaly Detection ◽  
Trajectory Analysis ◽  
False Positive Rate ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Asia Pacific ◽  
Detection Accuracy ◽  
Abnormal Behaviour ◽  
Positive Rate ◽  
Reception Area

<span lang="EN-GB">Abnormal behaviour detection has attracted signification amount of attention in the past decade due to increased security concerns around the world. The amount of data from surveillance cameras have exceeded human capacity and there is a greater need for anomaly detection systems for crime monitoring. This paper proposes a solution to this problem in a reception area context by using trajectory extraction through Gaussian Mixture Models and Kalman Filter for data association. Here, trajectory analysis was performed on extracted trajectories to detect four different anomalies such as entering staff area, running, loitering and squatting down. The developed anomaly detection algorithms were tested on videos captured at Asia Pacific University’s reception area. These algorithms were able to achieve a promising detection accuracy of 89% and a false positive rate of 4.52%.</span>

Block-Wise Background Subtraction Based on Gaussian Mixture Models

2014 ◽  
Vol 490-491 ◽  
pp. 1221-1227
Author(s):  
Mehran Yazdi ◽  
Mohammad A. Bagherzadeh ◽  
Mehdi Jokar ◽  
Mohammad A. Abasi
Keyword(s):  
Video Processing ◽  
Background Subtraction ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Moving Object ◽  
Repetitive Motion ◽  
Morphological Filtering ◽  
Real Time Tracking ◽  
The Difference ◽  
Experimental Findings

The background subtraction of an image enables us to distinguish a moving object in a video sequence and enter higher levels of video processing. Background processing is an essential strategy for many video processing applications and its most primary method (utilized to determine the difference of sequential frames) is very rapid and easy, but not appropriate for complicated scenes. In this article we introduce a method to remove the false distinction of the foreground. In our proposed method the updating of which is automatic, a mixture of Gaussians have been used. In addition this method depends on the passage of time. The previously introduced methods are often based on processing on the pixel and ignore the neighbor pixels in order to improve the background. Our method does not make do with one pixel but rather benefits from a block of pixels in order to include all the pixels included in the block. Experimental findings indicate considerable developments in the proposed method which can quickly and without morphological filtering model the background using image supervising cameras placed in both inside and outside locations and lighting changes, repetitive motion from clutter and scene changes. Subsequently the moving object in the scene can appear for real-time tracking and recognition applications.

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