Background Subtraction Technique Research Articles

Crystallographic Characterisation of Ultra-Thin, or Amorphous Transparent Conducting Oxides-The Case for Raman Spectroscopy.

The electronic and optical properties of transparent conducting oxides (TCOs) are closely linked to their crystallographic structure on a macroscopic (grain sizes) and microscopic (bond structure) level. With the increasing drive towards using reduced film thicknesses in devices and growing interest in amorphous TCOs such as n-type InGaZnO (IGZO), ZnSnO (ZTO), p-type CuCrO, or ZnRhO, the task of gaining in-depth knowledge on their crystal structure by conventional X-ray diffraction-based measurements are becoming increasingly difficult. We demonstrate the use of a focal shift based background subtraction technique for Raman spectroscopy specifically developed for the case of transparent thin films on amorphous substrates. Using this technique we demonstrate, for a variety of TCOs CuO, a-ZTO, ZnO:Al), how changes in local vibrational modes reflect changes in the composition of the TCO and consequently their electronic properties.

Vehicle tracking and speed estimation using view-independent traffic cameras

The traditional method's speed estimation accuracy depends on environmental changes and the new CNN technique accurately estimates speed only for quality video but input lacks, which leads to overfitting. Hence proposed method uses the Kalman filter and Hungarian algorithm to track detected vehicles for estimating the speed. First, vehicles are detected using the k-nearest neighbour (kNN) classifier-based background subtraction technique. Then logistic regression value is mapped to a binary value called decision threshold which accurately detects the vehicles. Secondly, each detected vehicle is tracked by a Kalman filter, then the Hungarian algorithm connects all the predictions and produces the tracking results. Finally, the vehicle speed is estimated by measuring the distance between the detected vehicle and the virtual line tracked vehicles on the ground plane. It also stores over-speeding vehicle images with speed. The proposed technique is verified with the BMVC standard database which shows a maximum error of ±2 kmph.

PIXEL-BASED FOREGROUND DETECTION IN REPETITIVE TIME-SERIES REGION

Currently, many state-of-the-art background subtraction techniques cannot deal properly with the area of periodic changing background, while some continue classifying them as foreground at intervals, others simply mask that area as a non-region of interest. To cope with this issue, a novel method of detecting repetitive temporal patterns based on the image sequences was proposed in this paper. The main emphasis of the proposed approach is on classifying those pixels as a background and identifying foreground objects in their relevant areas. As for the foreground detection, a model of time series pattern found in each pixel is individually built first; and then, any changes beyond the allowance of model periodicity are then determined as foreground objects. The proposed method could be used and run in parallel with any state-of-the-art background subtraction technique, allowing more accurate foreground-background segmentation. Experimental results showed that using Y channel, the proposed method of detecting time-series background area could achieve 92.9% of recall rate with less than 1% false positives. The recall of foreground detection in an area of repetitive time-series pattern was about 87%; while F-measure was about 0.73 on average. The false positives of foreground detection were also less than 1%. Accordingly, the proposed time-delay detection technique could significantly help to suppress the foreground error on time series background area, especially during the change from one sub-pattern to another which causes a camera sensor to capture both sub-pattern values in one frame. Performance comparison with state-of-the-art methods showed that our proposed method was able to reduce 80% of the average false alarm and improve F-measure to 28% while the computational efficiency was reduced by only 1%.

Pedestrian detection using a moving camera: A novel framework for foreground detection

While background subtraction techniques have been widely applied to detect moving objects in a video stream captured by a static camera, detecting moving objects using a moving camera still represents a challenging task. In this context, pedestrian detection using a camera placed on the top of a vehicle’s windshield has been rarely investigated. This is mainly due to the background ego-motion. Since the scene captured by the camera seems in motion, it is very difficult to distinguish the moving pedestrians from the others that belong to the static part of the scene. For this reason, a compensation step is needed to suppress the ego-motion. This paper presents a study on the main challenges facing pedestrian detection systems as well as methods proposed to handle these challenges. A novel trajectory classification framework for detecting pedestrians even in challenging real-world environments is proposed. The proposed method models the background motion between two consecutive frames in order to compensate the camera motion. Then, it defines a classification process that differentiates between the background and the foreground in the frame. Using the defined foreground, we consequently identify the presence of pedestrians in the scene. The proposed method was validated on a public benchmark dataset: CVC-14 containing both visible and far infrared video sequences in day and night times. Experimental results confirm the effectiveness of the proposed approach in capturing the dynamic aspect between frames and therefore detecting the presence of pedestrians in the scene.

Robust Detection of Abandoned Object for Smart Video Surveillance in Illumination Changes.

Most existing abandoned object detection algorithms use foreground information generated from background models. Detection using the background subtraction technique performs well under normal circumstances. However, it has a significant problem where the foreground information is gradually absorbed into the background as time passes and disappears, making it very vulnerable to sudden illumination changes that increase the false alarm rate. This paper presents an algorithm for detecting abandoned objects using a dual background model, which is robust even in illumination changes as well as other complex circumstances like occlusion, long-term abandonment, and owner re-attendance. The proposed algorithm can adapt quickly to various illumination changes. And also, it can precisely track the target objects to determine whether it is abandoned regardless of the existence of foreground information and the effect from the illumination changes, thanks to the largest-contour-based presence authentication mechanism proposed in this paper. For performance evaluation, we trialed the algorithm with the PETS2006, ABODA datasets as well as our dataset, especially to demonstrate its robustness in various illumination changes.

Interpretation of Formal Semantics from Hand Gesture to Text using Proficient Contour Tracing Technique

As an assorted nation with numerous religion tongues, India has attempted to embrace an official, institutionalized gesture based communication. Where as in Indo-Pakistani communication through signing, is viewed as the prevalent sort utilized in South Asia. Many who are deaf or hard of hearing rely on sign language, to communicate. However the estimation of sign language are very unsophisticated and definitions of what counts as proficiency that varies depends on many factors. There are many existing systems which use shape parameters like orientation, palm centroid ,data gloves with 5 accelerometer sensors , and optical markers which reflect infrared light to recognise hand gestures of sign language. Background subtraction techniques used in these systems are K-means clustering ,boundary counters, Eigen backgrounds using Eigen values and wireless technology and bluetooth for connecting software for transmitting recognised hand gesture signals. They are not cost effective but, the accuracy is not met to the need. Whereas, In our proposed system we concentrate mainly to convert hand gestures to text using contour tracing technique to recognise hand gestures using normal webcam. The semantics are classified by support vector machine with trained datasets. The recognised hand gestures are displayed as text. Our main objective is to resolve the problem of facing interviewer for vocally impaired individuals. This helps them to build their confidence and eradicate their inferiority complex compared to other methods

Gait identification by convolutional neural networks and optical flow

Non-interactive biometric systems have gained an enormous interest from computer vision researchers as they provide more efficient and reliable ways of identification and authorization from a distance. Gait and face recognition are types of non-interactive biometric systems without users’ cooperation with the surveillance system. On the contrary to face recognition, gait recognition can manage low-resolution and low-brightness images. It aims to know the individuals based on their style and way of walking. Gait recognition has numerous applications in several domains, such as healthcare monitoring, security systems, and surveillance systems for indoor and outdoor activities. Yet, gait recognition performance is frequently deteriorated by some variety of factors, such as viewing angle variations, and clothing changes. Recently, deep learning models have been employed efficiently in gait recognition systems. They are more generic, since the feature construction process is completely automated. This paper presents gait features measured automatically in the midst of walking for the recognition system. To extract these features from a video of a moving object, two vital modules are used, namely the motion detection and tracking, and the feature extraction. Accordingly, the principal module serves to distinguish the walking style in an image sequence or video. A background subtraction technique is executed to fragment the movement of the background, and the moving area related to the spatial silhouette is correctly tracked and segmented. The second module “Feature Extraction” is used to extract the features from the sequence of silhouette images. The gait cycle is calculated from the shape changes of the silhouettes, and it is used to construct a small sequence of Gait Energy Images (GEI). The optical flow of the GEI is measured to extract only the moving parts and exclude the static ones. Finally, the Convolution Neural Network (CNN) is fed with the optical flow output to build unique features. These features are used for neural network training, and evaluation is performed on popular gait benchmark datasets. The obtained results reveal an accuracy level of 95% with more resistance to view and probe changes.

EventFinder: a program for screening remotely captured images.

Camera traps are becoming ubiquitous tools for ecologists. While easily deployed, they require human time to organize, review, and classify images including sequences of images of the same individual, and non-target images triggered by environmental conditions. For such cases, we developed an automated computer program, named EventFinder, to reduce operator time by pre-processing and classifying images using background subtraction techniques and color histogram comparisons. We tested the accuracy of the program against images previously classified by a human operator. The automated classification, on average, reduced the data requiring human input by 90.8% with an accuracy of 96.1%, and produced a false positive rate of only 3.4%. Thus, EventFinder provides an efficient method for reducing the time for human operators to review and classify images making camera trap projects, which compile a large number of images, less costly to process. Our testing process used medium to large animals, but will also work with smaller animals, provided their images occupy a sufficient area of the frame. While our discussion focuses on camera trap image reduction, we also discuss how EventFinder might be used in conjunction with other software developments for managing camera trap data.

MOVING OBJECT DETECTION USING SPATIAL CORRELATION IN LAB COLOUR SPACE

Abstract. Background subtraction-based techniques of moving object detection are very common in computer vision programs. Each technique of background subtraction employs image thresholding algorithms. Different thresholding methods generate varying threshold values that provide dissimilar moving object detection results. A majority of background subtraction techniques use grey images which reduce the computational cost but statistics-based image thresholding methods do not consider the spatial distribution of pixels. In this study, authors have developed a background subtraction technique using Lab colour space and used spatial correlations for image thresholding. Four thresholding methods using spatial correlation are developed by computing the difference between opposite colour pairs of background and foreground frames. Out of 9 indoor and outdoor scenes, the object is detected successfully in 7 scenes whereas existing background subtraction technique using grey images with commonly used thresholding methods detected moving objects in 1–5 scenes. Shape and boundaries of detected objects are also better defined using the developed technique.

Real-time motion trajectory based head-on crash probability estimation on two-lane undivided highway

This paper endeavours to develop a model that estimates head-on crash probability from classified vehicle trajectory. The model formulation considered (1) drivers’ overtaking decision (OD) and (2) time-to-collision (TTC) on two-lane undivided highways. Drivers’ overtaking decision was modelled using nonlinear random parameter multivariate binary logistic regression. It considered traffic and drivers’ characteristics (i.e., aggressiveness) variables. In contrast, TTC was determined using a new formulation adding vehicles’ dynamic acceleration in consideration. Incorporation of overtaking importance factor (OIF) and crash frequency parameter (CFP) enabled the estimation of crash probability combining OD and TTC. Background subtraction technique along with Kalman filter was used to obtain vehicle trajectories from real-time video. Variable inputs required for calibrating the OD model were generated by constructing adjacency matrices among the vehicles. Exploiting these inputs, Metropolis-Hastings algorithm from Markov chain Monte Carlo (MCMC) method was applied to obtain calibrated parameters of the OD model for different types of vehicle. Calibration result showed that subject vehicle speed and the subject-opposing vehicle play an important role in influencing the overtaking decision. Moreover, the study also found that bus has maximum crash probability. Finally, the nomographs established in this paper ensures easy determination of the crash probability for practical applications.

Advanced wang–landau monte carlo‐based tracker for abrupt motions

Conventional tracking methods rarely consider abrupt motions and easily fail to track the abrupt motion of an object because they are based on the assumption of smooth motion. To assuage this problem, we propose a novel tracking algorithm combining the background subtraction method with the Wang–Landau Monte Carlo (WLMC) sampling method for dealing with abrupt motions effectively. First, the visual background extractor (ViBe), a background subtraction technique, is introduced to detect the object roughly. Second, we integrate the density‐of‐states term and ViBe information into a WLMC‐based tracking framework. By employing the density‐of states term and ViBe information, the Wang–Landau sampling scheme efficiently tracks the target in the whole state space in a short time. Third, in order to obtain good performance in terms of scalability, an independent fast scale estimation method is applied. Experimental results in both quantitative and qualitative measures demonstrate that our algorithm efficiently samples and tracks the target not only in position but also in scale variance accurately and robustly. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Soccer Players Detection Using GDLS Optimization and Spatial Bitwise Operation Filter

Advancement computer vision technology in order to help coach creates strategy has been affecting the sport industry evolving very fast. Players movement patterns and other important behavioral activities regarding the tactics during playing the game are the most important data obtained in applying computer vision in Sport Industry. The basic technique for extracting those information during the game is player detection. Three fundamental challenges of computer vision in detecting objects are random object’s movement, noise and shadow. Background subtraction is an object’s detection method that used widely for separating moving object as foreground and non moving object as background. This paper proposed a method for removing shadow and unwanted noise by improving traditional background subtraction technique. First, we employed GDLS algorithm to optimize background-foreground separation. Then, we did filter shadows and crumbs-like object pixels by applying digital spatial filter which is created from implementation of digital arithmetic algorithm (bitwise operation). Finally, our experimental result demonstrated that our algorithm outperform conventional background subtraction algorithms. The experiments result proposed method has obtained 80.5% of F1-score with average 20 objects were detected out of 24 objects.

Fast tracking of moving objects using single-pixel imaging

Successive images of a scene are captured and then further processed to achieve the moving object tracking. However, due to modulation rate limitations of the spatial light modulator in single-pixel imaging (SPI) system, the imaging frame rate cannot meet the high-resolution and real-time requirements for object tracking. In this paper, we demonstrate a fast object tracking technique based on SPI with an ultra-low sampling rate that is independent of imaging. We construct modulation information that satisfies the projection conditions and can transform 2D images into 1D projection curves. The 1D projection curves, which provide the location information of the moving object, can be obtained with high resolution in real-time, and then the tracking of the moving object is realized. A background subtraction technique for tracking moving objects that removes static components from a scene is also proposed. The proposed technique is verified by computational simulations and laboratory experiments. In the laboratory experiments, we demonstrate that the proposed method can be used to track moving objects with less than 0.2% of the measurements established by the Nyquist criterion, and it presents 256×256 pixels at ∼177 fps. The reported technique accelerates the tracking speed of SPI and provides an efficient strategy for remote sensing and biomedical applications.

Performance analysis of moving object detection using BGS techniques in visual surveillance

Over the last decennium, the object detection is the pivotal step in any machine vision and image processing application. It is the initial step applied to extract most informative pixel from the video stream. Many algorithms are available in literature for extraction of visual information or foreground object from video sequence. This paper also provides a detailed overview of both conventional and traditional approaches used for detection of object. This paper explores various related methods, major challenges, applications, resources such as datasets, web-sources, etc. This paper presents a detailed overview of a moving object detection using background subtraction techniques in the video surveillance system that provide safety in cities, towns or home when video sequence is captured using IP cameras. The experimental work of this paper is performed over change detection, I2R, and wallflower datasets. The experimental work also depicts a comparative analysis of some of the peer methods. This work demonstrates several performance metrics to check robustness of the compared state-of-the-art methods.

Moving Target Detection Based on Improved Gaussian Mixture Background Subtraction in Video Images

In recent years, background subtraction techniques have been used in vision and image applications for moving target detection. However, most methods cannot provide fine results due to dynamic backgrounds, noise, etc. The Gaussian mixture model (GMM) is a background modeling method commonly used in moving target detection. The traditional GMM method is vulnerable to noise interference, especially from dynamic backgrounds; thus, its detection performance is not good. Because of the influence of background noise and dynamic effects on moving target detection, we propose a method of moving target detection for dynamic backgrounds based on improved GMM background subtraction. This method can be divided into three stages. First, in the background modeling stage, to facilitate calculation and improve modeling speed, the video frame is blocked, and the background model is reconstructed using the image block averaging method. Second, in the moving target detection stage, the method of combining wavelet semi-threshold function denoising with mathematical morphology closed operation is used for denoising, which effectively eliminates the influence of noise and improves the detection effect. Third, in the background updating stage, the adaptive background updating method is used to update the background to improve detection results. The simulation results show that the improved method can reduce noise and dynamic background interference while improving moving target detection, thereby proving the effectiveness and adaptability of the proposed method.

Dynamic ARMA-Based Background Subtraction for Moving Objects Detection

Background subtraction is a prevailing method for moving object detection in videos with stationary backgrounds. However, accurate and real-time moving object detection is challenging in the presence of complex dynamic scenes. This paper presents a novel technique for background subtraction based on the dynamic autoregressive moving average (ARMA) model. Specifically, we utilize the temporal and spatial correlation of images in a video sequence to model each pixel to accurately model the background image's dynamic characteristics. In addition, we apply an adaptive least mean square (LMS) scheme to update the parameters of the background model to offset the dramatically dynamic characteristic of the background. The proposed algorithm is evaluated on two publicly available benchmark datasets with complex dynamic backgrounds. The experimental results show that this technique is robust and effective for background subtraction in complex dynamic backgrounds and is a promising moving object detection scheme for real-time visual surveillance.

Nonparametric background modelling and segmentation to detect micro air vehicles using RGB-D sensor

A novel approach to detect micro air vehicles in GPS-denied environments using an external RGB-D sensor is presented. The nonparametric background subtraction technique incorporating several innovative mechanisms allows the detection of high-speed moving micro air vehicles by combining colour and depth information. The proposed method stores several colour and depth images as models and then compares each pixel from a frame with the stored models to classify the pixel as background or foreground. To adapt to scene changes, once a pixel is classified as background, the system updates the model by finding and substituting the closest pixel to the camera with the current pixel. The background model update presented uses different criteria from existing methods. Additionally, a blind update model is added to adapt to background sudden changes. The proposed architecture is compared with existing techniques using two different micro air vehicles and publicly available datasets. Results showing some improvements over existing methods are discussed.

Illumination Invariant Motion Detection and Tracking Using SMDWT and a Dense Disparity-Variance Method

The navigation management systems in autonomous vehicles should be able to gather solid information about the immediate environment of the vehicle, discern ambulance from a delivery truck, and react in a proper manner to handle any difficult situation. Separating such information from a vision controlled system is a computationally demanding task for heavy traffic areas in the real world environmental conditions. In such a scenario, we need a robust moving object detection tracking system. To achieve this, we can make use of stereo vision-based moving object detection and tracking, utilizing symmetric mask-based discrete wavelet transform to deal with illumination changes, low memory requirement, and fake motion avoidance. The accurate motion detection in complex dynamic scenes is done by the combined background subtraction and frame differencing technique. For the fast motion track, we can employ a dense disparity-variance method. This SMDWT-based object detection has a maximum and minimum accuracy of 99.62% and 94.95%, respectively. The motion track has the highest accuracy of 79.47% within the time frame of 28.03 seconds. The lowest accuracy of the system is 62.01% within the time frame of 34.46 seconds. From the analysis, it is clear that this proposed method exceptionally outperforms the existing monocular and dense stereo object tracking approaches in terms of low computational cost, high accuracy, and in handling the dynamic environments.

Moving Object Detection in Video via Hierarchical Modeling and Alternating Optimization.

In conventional wisdom of video modeling, background is often treated as the primary target and foreground is derived using the technique of background subtraction. Based on the observation that foreground and background are two sides of the same coin, we propose to treat them as peer unknown variables and formulate a joint estimation problem, called Hierarchical modeling and Alternating Optimization (HMAO). The motivation behind our hierarchical extensions of background and foreground models is to better incorporate a priori knowledge about the disparity between background and foreground. For background, we decompose it into temporally low-frequency and high-frequency components for the purpose of better characterizing the class of video with dynamic background; for foreground, we construct a Markov random field prior at a spatially low resolution as the pivot to facilitate noise-resilient refinement at higher resolutions. Built on hierarchical extensions of both models, we show how to successively refine their joint estimates under a unified framework known as alternating direction multipliers method. Experimental results have shown that our approach produces more discriminative background and demonstrates better robustness to noise than other competing methods. When compared against current state-of-the-art techniques, HMAO achieves at least comparable and often superior performance in terms of F-measure scores especially for video containing dynamic and complex background.

SDSS-IV MaNGA: signatures of halo assembly in kinematically misaligned galaxies

We investigate the relationship of kinematically misaligned galaxies with their large-scale environment, in the context of halo assembly bias. According to numerical simulations, halo age at fixed halo mass is intrinsically linked to the large-scale tidal environment created by the cosmic web. We investigate the relationship between distances to various cosmic web features and present-time gas accretion rate. We select a sub-sample of ~900 central galaxies from the MaNGA survey with defined global position angles (PA; angle at which velocity change is greatest) for their stellar and H$\alpha$ gas components up to a minimum of 1.5 effective radii ($R_e$). We split the sample by misalignment between the gas and stars as defined by the difference in their PA. For each central galaxy we find its distance to nodes and filaments within the cosmic web, and estimate the host halo's age using the central stellar mass to total halo mass ratio $M_{*}/M_{h}$. We also construct halo occupation distributions using a background subtraction technique for galaxy groups split using the central galaxy's kinematic misalignment. We find, at fixed halo mass, no statistical difference in these properties between our kinematically aligned and misaligned galaxies. We suggest that the lack of correlation could be indicative of cooling flows from the hot halo playing a far larger role than `cold mode' accretion from the cosmic web or a demonstration that the spatial extent of current large-scale integral field unit (IFU) surveys hold little information about large-scale environment extractable through this method.