Background Subtraction Technique Research Articles

Hybrid Semiconductor Wafer Inspection Framework via Autonomous Data Annotation

Abstract In smart manufacturing, semiconductors play an indispensable role in collecting, processing, and analyzing data, ultimately enabling more agile and productive operations. Given the foundational importance of wafers, the purity of a wafer is essential to maintain the integrity of the overall semiconductor fabrication. This study proposes a novel automated visual inspection (AVI) framework for scrutinizing semiconductor wafers from scratch, capable of identifying defective wafers and pinpointing the location of defects through autonomous data annotation. Initially, this proposed methodology leveraged a texture analysis method known as gray-level co-occurrence matrix (GLCM) that categorized wafer images—captured via a stroboscopic imaging system—into distinct scenarios for high- and low-resolution wafer images. GLCM approaches further allowed for a complete separation of low-resolution wafer images into defective and normal wafer images, as well as the extraction of defect images from defective low-resolution wafer images, which were used for training a convolutional neural network (CNN) model. Consequently, the CNN model excelled in localizing defects on defective low-resolution wafer images, achieving an F1 score—the harmonic mean of precision and recall metrics—exceeding 90.1%. In high-resolution wafer images, a background subtraction technique represented defects as clusters of white points. The quantity of these white points determined the defectiveness and pinpointed locations of defects on high-resolution wafer images. Lastly, the CNN implementation further enhanced performance, robustness, and consistency irrespective of variations in the ratio of white point clusters. This technique demonstrated accuracy in localizing defects on high-resolution wafer images, yielding an F1 score greater than 99.3%.

BSEO-Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices.

Microfluidic models have become essential instruments for studying enhanced oil recovery techniques through fluid and chemical injection into micromodels to observe interactions with pore structures and resident fluids. The widespread use of cost-effective lab-on-a-chip devices, known for efficient data extraction and minimal reagent usage, has driven demand for efficient data management methods crucial for high-performance data and image analyses. This article introduces a semiautomatic method for calculating oil recovery in polymeric nanofluid flooding experiments based on the background subtraction (BSEO). It employs the background subtraction technique, generating a foreground binary mask to detect injected fluids represented as pixel areas. The pixel difference is then compared to a threshold value to determine whether the given pixel is foreground or background. Moreover, the proposed method compares its performance with two other representative methods: the ground truth (manual segmentation) and Fiji-ImageJ software. The experiments yielded promising results. Low values of mean-squared error (MSE), mean absolute error (MAE), and root-mean-squared error (RMSE) indicate minimal prediction errors, while a substantial coefficient of determination (R2) of 98% highlights the strong correlation between the method's predictions and the observed outcomes. In conclusion, the presented method emphasizes the viability of BSEO as a robust alternative, offering the advantages of reduced computational resource usage and faster processing times.

OPTIMAL TRAFFIC CONTROL SYSTEM FOR TRAFFIC CONGESTION

A smart city's traffic management system is regarded as one of its primary components. Traffic jams are a common sight on the roadways in metropolitan areas due to the rapid increase in population and urban mobility. In order to address road traffic management issues and assist authorities with appropriate planning, an intelligent traffic management system utilizing the Yolo algorithm and Open CV approach is proposed in this project. A workable model for counting automobiles in traffic was developed using image processing as the basis. image processing methods classified and tallied moving vehicles in traffic scene video streams captured by stationary cameras. The following is the detection and tracking methodology. The adaptive background subtraction technique is initially used to separate the moving vehicles from the traffic scene. Using videos to isolated picture blobs are recognized as individual vehicles once the background is subtracted. Following blob identification, vehicles in a certain area are counted and classified. A count of vehicles was observed with an accuracy of ideal camera calibration. To support the goal, data is gathered from video footage of vehicles traveling toward and away from the camera in order to count and use signal switching. The created system's results demonstrate that, with more enhancements, it applicable to count and categorize vehicles in real-time. After that, an optimization framework makes use of these predictions to dynamically modify signal timings in response to shifting traffic conditions. The optimization method seeks to increase overall traffic flow efficiency, decrease delays, and shorten travel times. To sum up, utilizing real-time traffic data to optimize signal control presents a viable approach to improving urban traffic management. This method makes use yolo techniques to facilitate the creation of flexible and effective traffic control, which in turn helps to create more sustainable and seamless urban transportation networks. KEYWORDS: Image classification, Video tracking, Information analysis, Vehicle detection, Signal Switching.

Adaptive Kalman Filter for Real-Time Visual Object Tracking Based on Autocovariance Least Square Estimation

Real-time visual object tracking (VOT) may suffer from performance degradation and even divergence owing to inaccurate noise statistics typically engendered by non-stationary video sequences or alterations in the tracked object. This paper presents a novel adaptive Kalman filter (AKF) algorithm, termed AKF-ALS, based on the autocovariance least square estimation (ALS) methodology to improve the accuracy and robustness of VOT. The AKF-ALS algorithm involves object detection via an adaptive thresholding-based background subtraction technique and object tracking through real-time state estimation via the Kalman filter (KF) and noise covariance estimation using the ALS method. The proposed algorithm offers a robust and efficient solution to adapting the system model mismatches or invalid offline calibration, significantly improving the state estimation accuracy in VOT. The computation complexity of the AKF-ALS algorithm is derived and a numerical analysis is conducted to show its real-time efficiency. Experimental validations on tracking the centroid of a moving ball subjected to projectile motion, free-fall bouncing motion, and back-and-forth linear motion, reveal that the AKF-ALS algorithm outperforms a standard KF with fixed noise statistics.

Complex Background Removal Method for Video of an Inclined Cable Based on UAV Detection of Structural Vibration Frequencies

The safety of inclined cables is fundamental to the integrity of cable-stayed bridges. The vibrational frequencies of these cables form the foundation for assessing the cable force. Traditional contact measurement methods necessitate the installation of sensors on each cable, incurring substantial costs. In scenarios where camera placement adjacent to an inclined cable is impractical, noncontact approaches such as video capture via unmanned aerial vehicles prove effective. However, unmanned aerial vehicle-captured videos present a challenge due to their complex background, impeding cable feature recognition. In our study, we initially utilized the Region Growing algorithm for background subtraction. To enhance this method, we integrated it with the unique structural characteristics of cables, leading to the creation of the RGv2 algorithm. This novel algorithm offers increased processing speed and improved accuracy. Furthermore, we combined our method with empirical mode decomposition for effective detection of cable frequency characteristics. We also implemented a hybrid method, combining the K-Means and line segment detector algorithms with empirical mode decomposition. Compared to deep learning techniques for background subtraction, our proposed method demonstrates superior computational efficiency and promising potential for measuring vibrational frequencies of inclined cables.

Efficient background subtraction method based on fast independent component analysis in video-surveillance

Modern video surveillance has now become an active area of research with a large set of requirements and various applications. In order to detect moving objects in video surveillance scenes, background subtraction techniques are the most used. In this paper, we developed and tested an efficient background subtraction technique in video surveillance based on the fast-independent component analysis (fast-ICA) method. The proposed technique initiated, first, on the use of a developed fast-ICA algorithm in order to estimate the de-mixing matrix and the denoising matrix parameters. Second, the estimated foreground can simply model by multiplying the data matrix with the de-mixing matrix. After that, the data matrix is multiplied by the denoising matrix for removing the noise. In addition, we propose a pre-processing and post-processing operations to effectively segment the true foreground objects and improve our results. The proposed method is evaluated on the publicly available change detection datasets CDnet 2012 and CDnet 2014 using performance parameters such as recall, precision and experimental results show that our algorithm can detect effectively and accurately the moving objects in several background and foreground conditions compared to other methods in literature with real-time frame rate.

Demonstration of neutrinoless double beta decay searches in gaseous xenon with NEXT

The NEXT experiment aims at the sensitive search of the neutrinoless double beta decay in 136Xe, using high-pressure gas electroluminescent time projection chambers. The NEXT-White detector is the first radiopure demonstrator of this technology, operated in the Laboratorio Subterráneo de Canfranc. Achieving an energy resolution of 1% FWHM at 2.6 MeV and further background rejection by means of the topology of the reconstructed tracks, NEXT-White has been exploited beyond its original goals in order to perform a neu- trinoless double beta decay search. The analysis considers the combination of 271.6 days of 136Xe-enriched data and 208.9 days of 136Xe-depleted data. A detailed background modeling and measurement has been developed, ensuring the time stability of the radiogenic and cosmogenic contributions across both data samples. Limits to the neutrinoless mode are obtained in two alternative analyses: a background-model-dependent approach and a novel direct background-subtraction technique, offering results with small dependence on the background model assumptions. With a fiducial mass of only 3.50 ± 0.01 kg of 136Xe-enriched xenon, 90% C.L. lower limits to the neutrinoless double beta decay are found in the T1/20ν\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {T}_{1/2}^{0\ u } $$\\end{document} > 5.5 × 1023− 1.3 × 1024 yr range, depending on the method. The presented techniques stand as a proof-of-concept for the searches to be implemented with larger NEXT detectors.

Analysis probes and statistical parameters affecting the OSL ages of mortar samples; a case study from Italy

Mortars stand among the very few materials that theoretically cannot be reused due to irreversible hardening processes. Among the various dating techniques, radiocarbon and Optically Stimulated Luminescence (OSL) struggle for being selected as the most robust and reliable dating technique for the specific material. The principle of mortar OSL dating depends on the bleaching of the quartz grains of the sandy aggregate while mixing and laying the mortar to daylight. Thus, the OSL ages strongly depend on the effective bleaching of the quartz. Here, we deal with OSL ages from the old Roman Theatre of Padua. Preliminary OSL results based on Single Aliquot Regeneration (SAR) protocol using three regenerated doses to multigrain (MG) quartz aliquots, indicated distributions of ED (Equivalent Dose) leading to significant mismatch of these results compared to the expected ages. The present work attempts to discuss the influence of various data analysis approaches such as (a) exclusion criteria like recycling ratio and recuperation values, (b) selection of the suitable region of interest in the signal's integral along with the correct background (late or early) subtraction technique and (c) appropriate statistical analysis by selecting the suitable age model (descriptive statistics, Central Age Model, Minimum Age Model) on the final distribution as well as both value and precision of the ED, enabling thus to obtain ages that are not only more accurate, but also closer to what was expected. Alternative statistical indicators of either the symmetry of the ED distributions (like skewness, kurtosis, etc) or the OSL signal itself (residual distribution analysis, kinetic parameters and percentage contribution of each OSL component to the overall signal) will be assessed in an effort to investigate possible correlations.

Development of Adaptive Realtime Mobile Based Traffic Management System for Congested Urban Cities

congestion in Nigeria, focusing on Port Harcourt. The study analyzed four main routes: Location, Rumuokoro, Rumuokwuta, and GRA Junction. Field surveys collected traffic volume and video recordings of cars traveling the indicated routes between 7 am and 7 pm. The Rivers State Command of the Federal Road Safety Corps provided further data. A mobile-based Traffic Management System was designed and simulated using Proteus. The technology transmits real-time traffic data from congested areas to a local server. This data is easily accessible on mobile devices. Python software was used to classify traffic congestion as heavy (0-14km/hr), medium (15-28km/hr), and free (29km/hr and above). Background subtraction was used for vehicle image analysis. The system can detect road traffic, correctly determine car speeds, count the number of vehicles, and send this traffic information to consumers via mobile devices via online communications. After simulating 1200 traffic data instances, 36 had mistaken, while 1164 were classified. This represents good accuracy, with 97% accuracy and 3% inaccuracy. The traffic data will be maintained in a database to help researchers identify places with recurring traffic congestion. These findings suggest that authorities should build alternative routes. It can also help build traffic-congestion-reducing policy review, organization, and enforcement initiatives. Keywords: Background Subtraction Technique, Mobile devices, Proteus, Traffic Congestion, Python

Background subtraction challenges in motion detection using Gaussian mixture model: a survey

<p>Motion detection is becoming prominent for computer vision applications. The background subtraction method that uses the Gaussian mixture model (GMM) is utilized frequently in camera or video settings. However, there is still more work that needs to be done to develop a reliable, accurate and high-performing technique due to various challenges. The degree of difficulty for this challenge is primarily determined by how the object to be detected is defined. It could be influenced by the changes in the object posture or deformations. In this context, we describe and bring together the most significant challenges faced by the background subtraction techniques based on GMM for dealing with a crucial background situation. Therefore, the findings of this study can be used to identify the most appropriate GMM version based on the crucial background situation.</p>

Highly sensitive portable laser absorption spectroscopy formaldehyde sensor using compact spherical mirror multi-pass cell

A portable sensor with an optical case dimension of 46 × 28 × 16 cm3 based on laser absorption spectroscopy for highly sensitive measurement of formaldehyde was reported. A compact improved spherical mirror multi-pass cell consisting of two 5-cm diameter spherical mirrors separated by 17.7 cm was developed. The cell offered a sample volume of 350 mL and an optical absorption path length of 50.6 m, giving a high path length-to-volume ratio of 14.6 cm−2. The sampling response time was less than 1 s, allowing the application of rapid spectral background subtraction technique to effectively suppress optical fringes. The achieved detection limit was 650 pptv (1σ, 1 s), corresponding to a minimum detection absorption coefficient of 2.3 × 10−9 cm−1. The capability of the HCHO sensor was demonstrated by two days of continuous indoor air monitoring. The developed portable sensor is suitable for mobile and fast indoor air quality measurements.

Comparison of two individual identification algorithms for snow leopards (Panthera uncia) after automated detection

Photo-identification of individual snow leopards (Panthera uncia) is the primary data source for density estimation via capture-recapture statistical methods. To identify individual snow leopards in camera trap imagery, it is necessary to match individuals from a large number of images from multiple cameras and historical catalogues, which is both time-consuming and costly. The camouflaged snow leopards also make it difficult for machine learning to classify photos, as they blend in so well with the surrounding mountain environment, rendering applicable software solutions unavailable for the species. To potentially make snow leopard individual identification available via an artificial intelligence (AI) software interface, we first trained and evaluated image classification techniques for a convolutional neural network, pose invariant embeddings (PIE) (a triplet loss network), and compared the accuracy of PIE to that of the HotSpotter algorithm (a SIFT-based algorithm). Data were acquired from a curated library of free-ranging snow leopards taken in Afghanistan between 2012 and 2019 and from captive animals in zoos in Finland, Sweden, Germany, and the United States. We discovered several flaws in the initial PIE model, such as a small amount of background matching, that was addressed, albeit likely not fixed, using background subtraction (BGS) and left-right mirroring (LR) techniques which demonstrated reasonable accuracy (Rank 1: 74% Rank-5: 92%) comparable to the Hotspotter results (Rank 1: 74% Rank 2: 84%)The PIE BGS LR model, in conjunction with Hotspotter, yielded the following results: Rank-1: 85%, Rank-5: 95%, Rank-20: 99%. In general, our findings indicate that PIE BGS LR, in conjunction with HotSpotter, can classify snow leopards more accurately than using either algorithm alone.

Traffic Rules Violation Detection System

Abstract: The paper presents a system that uses surveillance videos to automatically detect bike riders without helmets by us-ing object segmentation and background subtraction techniques. A consolidation approach is introduced to improve accuracy, and three feature representations are compared. The system achieves a detection accuracy of 93.80 percentage and has an average processing time of 11.58 milliseconds, making it a cost-effective and real-time solution for managing traffic violations related to helmet usage

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Cell polarity is a macroscopic phenomenon established by a collection of spatially concentrated molecules and structures that culminate in the emergence of specialized domains at the subcellular level. It is associated with developing asymmetric morphological structures that underlie key biological functions such as cell division, growth, and migration. In addition, the disruption of cell polarity has been linked to tissue-related disorders such as cancer and gastric dysplasia. Current methods to evaluate the spatiotemporal dynamics of fluorescent reporters in individual polarized cells often involve manual steps to trace a midline along the cells' major axis, which is time consuming and prone to strong biases. Furthermore, although ratiometric analysis can correct the uneven distribution of reporter molecules using two fluorescence channels, background subtraction techniques are frequently arbitrary and lack statistical support. This manuscript introduces a novel computational pipeline to automate and quantify the spatiotemporal behavior of single cells using a model of cell polarity: pollen tube/root hair growth and cytosolic ion dynamics. A three-step algorithm was developed to process ratiometric images and extract a quantitative representation of intracellular dynamics and growth. The first step segments the cell from the background, producing a binary mask through a thresholding technique in the pixel intensity space. The second step traces a path through the midline of the cell through a skeletonization operation. Finally, the third step provides the processed data as a ratiometric timelapse and yields a ratiometric kymograph (i.e., a 1D spatial profile through time). Data from ratiometric images acquired with genetically encoded fluorescent reporters from growing pollen tubes were used to benchmark the method. This pipeline allows for faster, less biased, and more accurate representation of the spatiotemporal dynamics along the midline of polarized cells, thus advancing the quantitative toolkit available to investigate cell polarity. The AMEBaS Python source code is available at: https://github.com/badain/amebas.git

Twin background subtraction technique: A novel infrared technique for simultaneous thickness and temperature measurements of micro films

The study proposes a novel non-intrusive infrared technique to measure simultaneously the thickness and temperature of a thin layer that is semi-transparent to infrared radiation. The measurement method has been called Twin Background Subtraction technique. The technique simultaneously exploits the mid-wave IR emission from the film to be measured and the radiation attenuation by the same film of the emission coming from two backgrounds at different constant temperatures. This study discusses the method theoretically and experimentally, focusing on the uncertainties and the dependence on different parameters of the set-up and fluid physical properties.The fluid adopted for the film is water, and various tests have been carried out by changing its temperature between 15 and 50 °C and its thickness from 0 to 500 µm. The TBS technique was proven to be of interest for studying micro to mini films both from a qualitative and quantitative point of view for water films ranging between 10 µm and 350 µm.

Kernel-Induced Possibilistic Fuzzy Associate Background Subtraction for Video Scene

The background subtraction (BGS) technique is popularly used for many surveillance systems, segmenting the foreground by subtracting the modeled background from the image sequences. The effectiveness of any BGS technique depends on the robustness of the constructed background model. It is to be noted that many BGS schemes are affected by the inclusion of either noisy pixels in background construction or parameters of generative models. In this regard, we propound an idea of a kernel-induced possibilistic fuzzy associated BGS scheme for local change detection from a fixed camera captured sequence. The proposed scheme follows two stages: background training and foreground segmentation. In the background construction stage, each pixel is modeled using a possibilistic fuzzy cost function in kernel-induced space. The use of the induced kernel function will project the low-dimensional data into a higher dimensional space and the use of the possibilistic function will construct a robust background model based on the density of the data in the temporal domain avoiding the noisy and outlier points. The performance of the proposed scheme is tested on three benchmark databases. The effectiveness of the proposed scheme is evaluated on different performance evaluation measures: precision, recall, F-measure, and average similarity. We corroborate our findings by comparing them against 19 state-of-the-art existing BGS techniques.

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Sea-surface object detection is critical for navigation safety of autonomous ships. Electro-optical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of these approaches. The article also proposes the direction of future research for sea-surface object detection based on EO sensors.

An integrated strategy to study the combination mechanisms of Bupleurum chinense DC and Paeonia lactiflora Pall for treating depression based on correlation analysis between serum chemical components profiles and endogenous metabolites profiles

Ethnopharmacological relevanceBupleurum chinense DC-Paeonia lactiflora Pall (BCD-PLP) is a common clinical herb pair in traditional Chinese medicine (TCM) prescriptions commonly used to treat depression. However, its combination mechanisms with its anti-depressive effects remain highly unclear. Aim of the studyHere, an effective strategy has been developed to study the combination mechanisms of Bupleurum chinense DC (BCD) and Paeonia lactiflora Pall (PLP) by integrating serum pharmacochemistry analysis, metabolomics technology, and molecular docking technology. Materials and methodsFirst, the depression model rats were replicated by the chronic unpredictable mild stress (CUMS) procedure, and the difference in the chemical composition in vivo before and after the combination of BCD and PLP was analyzed by integrating background subtraction and multivariate statistical analysis techniques. Then, UPLC/HRMS-based serum metabolomics was performed to analyze the synergistic effect on metabolite regulation before and after the combination of BCD and PLP. Further, the correlation analysis between the differential exogenous chemical components and the differential endogenous metabolites before and after the combination was employed to dissect the combination mechanisms from a global perspective of combining metabolomics and serum pharmacochemistry. Finally, the molecular docking between the differential chemical components and the key metabolic enzymes was applied to verify the regulatory effect of the differential exogenous chemical components on the differential endogenous metabolites. ResultsThe serum pharmacochemistry analysis results demonstrated that the combination of BCD and PLP could significantly affect the content of 10 components in BCD (including 5 prototype components were significantly decreased and 5 metabolites were significantly increased) and 8 components in PLP (including 4 prototype components and 3 metabolites were significantly increased, 1 metabolite was significantly decreased), which indicated that the combination could enhance BCD prototype components' metabolism and the absorption of the PLP prototype components. Besides, metabolomics results indicated that the BCD-PLP herb pair group significantly reversed more metabolites (8) than BCD and PLP single herb group (5 & 4) and has a stronger regulatory effect on metabolite disorders caused by CUMS. Furthermore, the correlation analysis results suggested that saikogenin F and saikogenin G were significantly positively correlated with the endogenous metabolite itaconate, an endogenous anti-inflammatory metabolite; and benzoic acid was significantly positively correlated with D-serine, an endogenous metabolite with an antidepressant effect. Finally, the molecular docking results further confirmed that the combination of BCD and PLP could affect the activities of cis-aconitic acid decarboxylase and D-amino acid oxidase by increasing the in vivo concentration of saikogenin F and benzoic acid, which further enhances its anti-inflammatory activity and anti-depressive effect. ConclusionsIn this study, an effective strategy has been developed to study the combination mechanisms of BCD and PLP by integrating serum pharmacochemistry analysis, multivariate statistical analysis, metabolomics technology, and molecular docking technology. Based on this strategy, the present study indicated that the combination of BCD and PLP could affect the activities of cis-aconitic acid decarboxylase and D-amino acid oxidase by increasing the concentration of saikogenin F and benzoic acid in vivo, which further enhances its anti-depressive effect. In short, this strategy will provide a reliable method for elucidating the herb-herb compatibility mechanism of TCM.

Extending the energy range of AstroSat-CZTI up to 380 keV with compton spectroscopy

ABSTRACT The CZTI (Cadmium Zinc Telluride Imager) onboard AstroSat is a high energy coded mask imager and spectrometer in the energy range of 20–100 keV. Above 100 keV, the dominance of Compton scattering cross-section in CZTI results in a significant number of 2-pixel Compton events and these have been successfully utilized for polarization analysis of Crab pulsar and nebula (and transients like Gamma-ray bursts) in 100–380 keV. These 2-pixel Compton events can also be used to extend the spectroscopic energy range of CZTI up to 380 keV for bright sources. However, unlike the spectroscopy in primary energy range, where simultaneous background measurement is available from masked pixels, Compton spectroscopy requires blank sky observation for background measurement. Background subtraction, in this case, is non-trivial because of the presence of both short-term and long-term temporal variations in the data, which depend on multiple factors like earth rotation and the effect of South Atlantic Anomaly (SAA) regions etc. We have developed a methodology of background selection and subtraction that takes into account for these effects. Here, we describe these background selection and subtraction techniques and validate them using spectroscopy of Crab in the extended energy range of 30–380 keV region, and compare the obtained spectral parameters with the INTEGRAL results. This new capability allows for the extension of the energy range of AstroSat spectroscopy and will also enable the simultaneous spectropolarimetric study of other bright sources like Cygnus X-1.

Detection of Change in Body Motion With Background Construction and Silhouette Orientation

Background subtraction techniques have been widely implemented and improvised to obtain a stable background model. The novelty of the proposed work is to generate a stable background model under dynamic changes in the environmental conditions where a) an improved background subtraction algorithm is proposed based on GMM with EM algorithm for computing granulometry and run faster for the generation of a stable background model; b) Detecting the foreground by curvelet based denoising process with improvised semisoft thresholding techniques with morphological operations; c) background maintenance is done by an adaptive algorithm in which the intensity values are mapped to remove the connected components with less than P pixels. The proposed scheme works for the Spatio-temporal motion of the object in both spatial and temporal modes. The experimental outcome for the proposed model results in the accurate shape analysis of the object in motion thereby dipping the complexity