Background Subtraction Algorithm Research Articles

A Dynamic Detection Method for Railway Slope Falling Rocks Based on the Gaussian Mixture Model Segmentation Algorithm

Rockfall intrusion detection is crucial for the safety management of railway operations, and video detection methods help reduce deployment costs and improve detection efficiency. Mainstream neural network-based video detection methods have rapidly evolved in recent years but struggle to adapt to complex scenarios such as existing railway slope constructions due to weak generalization ability, low accuracy, and limited information acquisition. Therefore, this paper introduces a dynamic neural network detection model and establishes a dataset for rockfall intrusions in existing railway slope scenarios. The model initially relies on the YOLOv5 neural network, adopting an activation function suitable for the target scenario and addressing overfitting to achieve precise target recognition. Based on the neural network, the model dynamically detects rolling rockfalls by integrating a background subtraction algorithm based on the Gaussian Mixture Model and captures target dimensions using monocular vision technology, thus broadening the dimensions of detection information. Trials conducted on a railway in Shandong, China, demonstrate that the model accurately identifies moving rockfalls along the railway slopes and acquires the dimensions of moving rockfalls, successfully filtering out low-risk targets in the scene.

Research on health monitoring and damage recognition algorithm of building structures based on image processing

With the continuous deepening of the urbanization process and the progress of science and technology, people transform nature and develop nature on a larger and larger scale, among which the most iconic transformation is a variety of building structures built by people. And with the passage of time, the building structure in the perennial wind and sun, there will be signs of “illness”, if not timely treatment, it will have a huge impact on the stability and safety of the building structure. Based on this, in this paper, according to the characteristics of crack identification on the surface of concrete structure, background subtraction algorithm is selected for image noise reduction processing. Through three steps of digital image noise reduction, crack extraction and crack parameter identification, the quantitative recognition of cracks is completed and a complete system of crack parameter identification is formed. The experimental results show that the machine learning model of building structure health monitoring and damage recognition algorithm proposed in this paper has excellent statistical performance, and the relative error accuracy of recognition can be controlled within 10%.

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.

Improve Multi-Object Detection and Tracking for an Automated Traffic Surveillance System

Multi-Object Detection and Tracking (MODT) are essential in many application fields. Still, many enhancements in the speed of detection and tracking were required to overcome the challenges during implementation. This paper presents a new algorithm system for (MODT) to improve the execution time to be robust in real-time applications. A background subtraction detection algorithm with a Kalman filter was used to track and predict the object position and speed parameters. To improve the processing time, its needs to reduce some frames in a way that does not affect the detection accuracy too much and instead use the prediction and the estimated value obtained based on the Kalman filter for the tracked object. This work uses a single video camera to show how effectively to compute and detect multiple objects concurrently; it is applied for daytime preprocessing in an automated traffic surveillance system. Preliminary testing findings show that the suggested algorithm for this vehicle monitoring system is feasible and effective. It illustrates that using the suggested algorithm with a single video camera can simultaneously watch, detect, and track several vehicles and improve execution time. Simulation results on the built system demonstrate that the proposed system reduced the execution time to approximately 41.5% compared to the standard background subtraction algorithm. Results indicate the proposed algorithm has an approximate error for the position and speed of detected and tracked objects compared with the standard background subtraction algorithm. Index Terms— multi-object detection, tracking, background subtraction, Kalman filter, morphological.

Moving Object Activator in Background Subtraction Algorithm for Automatic Passenger Counter System in Public Transportation

Buses are the most used transportation by people in Indonesia when traveling between cities. However, to improve passenger comfort, a tool is required to determine the number of passengers on the bus. This research presents an automated system to count the number of passengers based on a background subtraction algorithm and moving object activator. This research aims to provide the number of passengers based on video images taken from the entranceway. The system is built with a camera, Raspberry Pi, and LCD. The APC system starts the counting process by removing the video background image from the captured object image. The entry and exit direction of the object is determined using the concept of moving object activator. The experiments were applied in several scenarios to determine the robustness of the system. The best APC performance was achieved when the system is positioned perpendicularly above the entranceway at a height of 230 cm and a light intensity of 800-1000 lux. Meanwhile, the moving object activator is effective in supporting the system's performance to determine the passenger's direction. In this scenario, the results stated that the accuracy of APC system performance reached 93.8%.

Алгоритм сегментации для выделения движущихся объектов на основе реконструкции фона с использованием параметров шумовых характеристик

One of the most important tasks for ensuring life safety is monitoring objects and tracking the behavior of people in urban infrastructure from an unmanned aerial vehicle (UAVs). Difficulties arise when the UAVs is used for surveillance in areas that are difficult or unsuitable for humans to access, and then there is a risk of accidents. Therefore, analysis, reconstruction and tracking of objects can help in solving existing cases. As a critical technology in computer vision and video processing, video object segmentation has far-reaching pragmatic and application implications. In this paper, we propose an algorithm for segmenting video objects based on background reconstruction for extracting moving objects from a video sequence taken from a UAVs. In the theoretical part of the work, methods for detecting and segmenting moving objects are considered based on background subtraction algorithms, the optical flow of the Kalman filter. An exact mask for detecting changes is proposed, taking into account the estimate of the threshold of noise characteristics, as well as an algorithm for detecting movements. A qualitative analysis of the background reconstruction error was carried out using a statistical criterion. A modified algorithm for obtaining a change detection mask (MDM) using a threshold estimated using noise characteristics is proposed. The results of segmentation of objects, calculation of the root-mean-square error of background reconstruction for the proposed method when extracting moving objects are shown. The developed method allows you to effectively recognize and extract moving objects in a video sequence. This algorithm is suitable for the case when the UAVs freezes in the air. After the background reconstruction, there are small defects that can be eliminated using morphological operations.

Implementasi IoT Pada Sistem Pembayaran di Koperasi Sekolah

Information technology has been widely adopted by numerous educational institutions, both formal and informal, based on religion or non-religious, The urgency of innovative technologi which appliable to school business processes are rising rapidly within enormous applicable internet of Things available. The research methodology in this research conducted comprehensively. Researcher will used the technolgu of Microcontroller Esp32 to help the implementation of payment in one of the department of the research object. Developing the system within the school system will need very trmendous effort to justify the system accepted by all the stakeholder. Researcher using the srum methods in software development and using the esp32 microcontroller as the thechologu to merged within, researcher hope the capablity of Background Subtraction Algorithm on ESP32, it is elucidated that the background subtraction algorithm can be implemented on the ESP32 device. This device possesses the capability to process image frames measuring 80 x 60 pixels a total of 10,000 times within a temporal span of 2,000 milliseconds (at an approximate rate of ±5,000 frames per secondThe result of this research supposedly achieve the implementation of the microcontroller technology to provide the solution in school business unit payment processes. All the visitor to the business unit are detected with the RFID card using the microcontroller to automate the identification and enabling better transaction process flow.

Efficient parallel implementation of Gaussian Mixture Model background subtraction algorithm on an embedded multi-core Digital Signal Processor

Scene segmentation is crucial in video processing applications, such as vehicle detection. The Gaussian Mixture Model (GMM) based background subtraction (BS) is a widely employed technique for scene segmentation. However, due to its high computational demands, an efficient parallel implementation is necessary. This paper proposes a parallelized implementation of the GMM algorithm on the C6678 digital signal processor (DSP), utilizing its eight cores. Three different parallelization approaches are presented: Open Multiprocessing (OpenMP), manually optimized method, and manually optimized method involving Direct Memory Access (DMA) engine. To evaluate their effectiveness, a metrics comparison is conducted using background truth images provided by the literature dataset references. The experimental results demonstrate that the proposed manually optimized method surpasses the current state-of-the-art. It achieves a remarkable parallel efficiency of approximately 99% for low and medium frame sizes and 83% and 79% for high-definition (HD) and full-HD resolution frames, respectively, when all eight cores are enabled. In contrast, the parallel efficiency obtained using OpenMP is only 45%.

NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector

Vertebrate retinas are highly-efficient in processing trivial visual tasks such as detecting moving objects, which still represent complex challenges for modern computers. In vertebrates, the detection of object motion is performed by specialised retinal cells named Object Motion Sensitive Ganglion Cells (OMS-GC). OMS-GC process continuous visual signals and generate spike patterns that are post-processed by the Visual Cortex. Our previous Hybrid Sensitive Motion Detector (HSMD) algorithm was the first hybrid algorithm to enhance Background subtraction (BS) algorithms with a customised 3-layer Spiking Neural Network (SNN) that generates OMS-GC spiking-like responses. In this work, we present a Neuromorphic Hybrid Sensitive Motion Detector (NeuroHSMD) algorithm that accelerates our HSMD algorithm using Field-Programmable Gate Arrays (FPGAs). The NeuroHSMD was compared against the HSMD algorithm, using the same 2012 Change Detection (CDnet2012) and 2014 Change Detection (CDnet2014) benchmark datasets. When tested against the CDnet2012 and CDnet2014 datasets, NeuroHSMD performs object motion detection at 720 × 480 at 28.06 Frames Per Second (fps) and 720 × 480 at 28.71 fps, respectively, with no degradation of quality. Moreover, the NeuroHSMD proposed in this article was completely implemented in Open Computer Language (OpenCL) and therefore is easily replicated in other devices such as Graphical Processing Units (GPUs) and clusters of Central Processing Units (CPUs).

Nonlinear stabilization and reference tracking of visual servo system using TS fuzzy augmented iterative learning control: Experimental validation

To address the nonlinear stabilization problem and improve the tracking control feature of ball on plate system (BPS), this paper puts forward a novel Takagi Sugeno (TS) fuzzy control augmented with the current cycle feedback iterative learning control (CCF-ILC) scheme. According to Bode’s sensitivity integral, the performance of linear controllers is always a trade-off between reference tracking and robustness. Hence, to deal with the so-called ‘waterbed’ effect, this work exploits the capability of TS fuzzy to handle the nonlinear dynamics and synthesizes a learning control scheme based on current iteration error to capitalize the information rich error signal for enhancing the robustness and trajectory tracking features. The global asymptotic stability of the proposed TS fuzzy augmented ILC scheme is proved using the Lyapunov function and linear matrix inequalities (LMIs). Moreover, the monotonic convergence of ILC is presented based on the singular value condition. For identifying the rolling mass from the video stream, a background subtraction algorithm based on thresholding technique is implemented. Finally, the robustness and tracking features of the proposed scheme are evaluated on a two degrees of freedom (DoF) laboratory scale BPS system through hardware in loop (HIL) testing for three realistic test cases. The tracking performance quantified using the root mean square error (RMSE) and power spectral density plot corroborates that the proposed scheme can offer better setpoint tracking and robustness feature compared to state-of-the-art fuzzy and ILC control techniques implemented on BPS.

A tracking system with multiple region background subtraction algorithm for night scene

A tracking system with multiple region background subtraction algorithm for night scene

New machine learning-based automatic high-throughput video tracking system for assessing water toxicity using Daphnia Magna locomotory responses

Daphnia magna is an important organism in ecotoxicity studies because it is sensitive to toxic substances and easy to culture in laboratory conditions. Its locomotory responses as a biomarker are highlighted in many studies. Over the last several years, multiple high-throughput video tracking systems have been developed to measure the locomotory responses of Daphnia magna. These high-throughput systems, used for high-speed analysis of multiple organisms, are essential for efficiently testing ecotoxicity. However, existing systems are lacking in speed and accuracy. Specifically, speed is affected in the biomarker detection stage. This study aimed to develop a faster and better high-throughput video tracking system using machine learning methods. The video tracking system consisted of a constant temperature module, natural pseudo-light, multi-flow cell, and an imaging camera for recording videos. To measure Daphnia magna movements, we developed a tracking algorithm for automatic background subtraction using k-means clustering, Daphnia classification using machine learning methods (random forest and support vector machine), and tracking each Daphnia magna location using the simple online real-time tracking algorithm. The proposed tracking system with random forest performed the best in terms of identification (ID) precision, ID recall, ID F1 measure, and ID switches, with scores of 79.64%, 80.63%, 78.73%, and 16, respectively. Moreover, it was faster than existing tracking systems such as Lolitrack and Ctrax. We conducted an experiment to observe the impact of toxicants on behavioral responses. Toxicity was measured manually in the laboratory and automatically using the high-throughput video tracking system. The median effective concentration of Potassium dichromate measured in the laboratory and using the device was 1.519 and 1.414, respectively. Both measurements conformed to the guideline provided by the Environmental Protection Agency of the United States; therefore, our method can be used for water quality monitoring. Finally, we observed Daphnia magna behavioral responses in different concentrations after 0, 12, 18, and 24 h and found that there was a difference in movement according to the concentration at all hours.

Reviewing ViBe, a Popular Background Subtraction Algorithm for Real-Time Applications

Background subtraction or foreground segmentation is commonly the first step in video processing pipelines, where foreground objects and moving entities are detected for further analysis. Background subtraction is still an open problem in computer vision due to the wide range of possible scenarios and challenging cases, such as weather conditions, illumination variation, dynamic backgrounds or low frame-rates. In this article, we review a well-known general algorithm for background subtraction named visual background extractor or ViBe. More than 10 years after its publication, ViBe is still widely used due to its simplicity and low computational load, as its authors claim it can process up to 200 fps. We analyze the algorithm's mechanism to segment foreground objects, comment on its strengths and drawbacks, and describe an effective variant of the method to quickly dissipate ghost effects using 3-frame difference. Furthermore, we provide an easy-to-use demo that allows to quickly test the performance of ViBe on custom data, modify its parameters and download the results. **This is an MLBriefs article, the source code has not been reviewed!**<br> The ViBe algorithm is covered by several patents and its code is not provided with this publication. <a href="https://orbi.uliege.be/handle/2268/145853">Its code is only shared for research purposes.</a><br>

Accurate Background Reduction in Adaptive Optical Three-Dimensional Stimulated Emission Depletion Nanoscopy by Dynamic Phase Switching

Stimulated emission depletion (STED) fluorescence nanoscopy allows the three-dimensional (3D) visualization of nanoscale subcellular structures, providing unique insights into their spatial organization. However, 3D-STED imaging and quantification of dense features are obstructed by the low signal-to-background ratio (SBR) resulting from optical aberrations and out-of-focus background. Here, combining adaptive optics elements, we present an easy-to-implement, flexible, and effective method to improve the SBR by dynamic phase switching. By switching to a counterclockwise vortex phase mask and a top-hat one with an incorrect inner radius, the depletion pattern features a nonzero-intensity center, enabling accurate background recordings. When the recorded background is subtracted from the aberration-corrected 3D-STED image, the SBR in dense sample areas can be improved by a factor of 3–7 times. We demonstrate our method on various dense subcellular structures, showing more advantages than the software-based background subtraction algorithms.

Performance analysis of U-Net with hybrid loss for foreground detection.

With the latest developments in deep neural networks, the convolutional neural network (CNN) has made considerable progress in the area of foreground detection. However, the top-rank background subtraction algorithms for foreground detection still have many shortcomings. It is challenging to extract the true foreground against complex background. To tackle the bottleneck, we propose a hybrid loss-assisted U-Net framework for foreground detection. A proposed deep learning model integrates transfer learning and hybrid loss for better feature representation and faster model convergence. The core idea is to incorporate reference background image and change detection mask in the learning network. Furthermore, we empirically investigate the potential of hybrid loss over single loss function. The advantages of two significant loss functions are combined to tackle the class imbalance problem in foreground detection. The proposed technique demonstrates its effectiveness on standard datasets and performs better than the top-rank methods in challenging environment. Moreover, experiments on unseen videos also confirm the efficacy of proposed method.

The information content of jet quenching and machine learning assisted observable design

Jets produced in high-energy heavy-ion collisions are modified compared to those in proton-proton collisions due to their interaction with the deconfined, strongly-coupled quark-gluon plasma (QGP). In this work, we employ machine learning techniques to identify important features that distinguish jets produced in heavy-ion collisions from jets produced in proton-proton collisions. We formulate the problem using binary classification and focus on leveraging machine learning in ways that inform theoretical calculations of jet modification: (i) we quantify the information content in terms of Infrared Collinear (IRC)-safety and in terms of hard vs. soft emissions, (ii) we identify optimally discriminating observables that are in principle calculable in perturbative QCD, and (iii) we assess the information loss due to the heavy-ion underlying event and background subtraction algorithms. We illustrate our methodology using Monte Carlo event generators, where we find that important information about jet quenching is contained not only in hard splittings but also in soft emissions and IRC-unsafe physics inside the jet. This information appears to be significantly reduced by the presence of the underlying event. We discuss the implications of this for the prospect of using jet quenching to extract properties of the QGP. Since the training labels are exactly known, this methodology can be used directly on experimental data without reliance on modeling. We outline a proposal for how such an experimental analysis can be carried out, and how it can guide future measurements.

A novel context-aware system to improve driver’s field of view in urban traffic networks

Principal objectives of the Intelligent Transportation Systems (ITS) are to improve traffic safety, facilitate informed traffic decision making, and enhance quality of life and services in a smart traffic environment. Vehicle crashes at urban traffic intersections are among the rudimentary sources of injuries and fatalities in the cities. According to the report of the World Health Organization (WHO), in every 25 seconds, one vulnerable road-user is being killed by a vehicle crash. Therefore, it is necessary to take a novel and smart approach for improving the safety and reducing vehicle crashes. This leads to a contextual perception and spatial awareness of driver to increase security and safety for the driver, vehicle, and road users. Autonomous vehicles collects the information from the environment through equipped sensors on the vehicle such as camera, laser, radar, and Global Navigation Satellite Systems (GNSS). The main challenge arises when the person or objects are located beyond the driver’s Field of View (FOV) and cannot be detected by embedded sensors on the vehicles. This paper proposes an Advanced Driver Assistance System (ADAS) to increase the safety on road intersections by taking advantage of existing infrastructures (e.g road camera) being used for traffic control. The aim of this research is improving the driver’s FOV using a computer vision approach (e.g background subtraction algorithm) and Location Based Service (LBS). The case study results at Tehran metropolitan demonstrate the reduction in traffic collision risk and improvement of pedestrian safety using the proposed system.

A Fog-Assisted Framework for Intelligent Video Preprocessing in Cloud-Based Video Surveillance as a Service

Video preprocessing is one of the vital steps before extracting useful information from surveillance videos. This article identifies the key issue in cloud-based video surveillance as a service (VSaaS). If the recording environment is almost static and storing 24/7 video recording leads to additional storage, computational and network complexity to the cloud user. To address this problem, the existing architecture of the VSaaS is thoroughly studied and proposed a fog-assisted framework for preprocessing the incoming video. The two important functionalities of this framework are intelligent frame-selection and frame-reconstruction. During frame-selection, the lightweight background subtraction algorithm based on random projection with structural similarity index manages to effectively select the necessary frames to store in the cloud. This framework also provides an option for the user to successfully reconstruct the full-length video using frame inbetweening method. The proposed algorithm is tested with CDNET-2014 and LASIESTA data. The results are evaluated in-terms of running time, selected frames per video, percentage of reduction and quality of video experiencing. The result shows that the proposed frame-selection manages to reduce nearly 60% of storage requirement for the CDNET-2014 data. The frame reconstruction algorithm reconstructs the full-length video without affecting the quality of video experiencing.

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

Object identification-based stall detection and stall legitimacy analysis for traffic patterns

Illegal short-term vehicle stalls are frequent in developing countries, which disturb smooth traffic flow and results in congestion, road blockage, and even lead to accidents. Short-term stalls span a few seconds—e.g., a vehicle stopping to make way for people crossing the road. However, traditional stall detection techniques detect stalls that span 10 s or more. Moreover, existing techniques, such as background subtraction algorithms, are vulnerable to illumination changes and indicate too many false stalls with smaller stopping criteria. Furthermore, for violation indication, post-stall analysis is required to filter out the legitimate stalls where a vehicle stops for some valid reason. We present a two-fold methodology to detect illegally parked vehicles. An object identification-based technique is employed over stationary images to detect stall. Furthermore, the legitimacy of the stall is determined by performing pedestrian detection and its position analysis. We tested our proposed approach using publicly available advanced video and signal based surveillance 2007 PV dataset and ViSOR dataset. The technique successfully detects stalls with a recall of 0.96, after weaving the stopping criterion from the ground truth.