Performance comparison of Background Estimation algorithms for detecting moving vehicle (original) (raw)
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A background subtraction algorithm for detecting and tracking vehicles
Expert Systems With Applications, 2011
An innovative system for detecting and extracting vehicles in traffic surveillance scenes is presented. This system involves locating moving objects present in complex road scenes by implementing an advanced background subtraction methodology. The innovation concerns a histogram-based filtering procedure, which collects scatter background information carried in a series of frames, at pixel level, generating reliable instances of the actual background. The proposed algorithm reconstructs a background instance on demand under any traffic conditions. The background reconstruction algorithm demonstrated a rather robust performance in various operating conditions including unstable lighting, different view-angles and congestion.
Evaluating the Performance of Common Background Subtraction Techniques
recognizing moving objects from a video stream considered to be a fundamental and critical task in many computer-vision applications. A common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model. This paper compares various background subtraction algorithms for detecting a single object. The work considers approaches varying from simple techniques such as static method and frame differencing to more sophisticated probabilistic modeling techniques such as adaptive median filtering and GMM. The evaluating process is based on visual observation of the output of the background subtraction techniques under assessments.
Robust techniques for background subtraction in urban traffic video
2004
Identifying moving objects from a video sequence is a fundamental and critical task in many computer-vision applications. A common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model. There are many challenges in developing a good background subtraction algorithm. First, it must be robust against changes in illumination. Second, it should avoid detecting non-stationary background objects such as swinging leaves, rain, snow, and shadow cast by moving objects. Finally, its internal background model should react quickly to changes in background such as starting and stopping of vehicles. In this paper, we compare various background subtraction algorithms for detecting moving vehicles and pedestrians in urban traffic video sequences. We consider approaches varying from simple techniques such as frame differencing and adaptive median filtering, to more sophisticated probabilistic modeling techniques. While complicated techniques often produce superior performance, our experiments show that simple techniques such as adaptive median filtering can produce good results with much lower computational complexity.
Relative comparison of Background Subtraction Techniques in Moving Object Detection
Moving object detection is a computer technology that deals with detection non stationary object in digital image & videos. There are many methods used to detect moving object like background subtraction, modified background subtraction, Gaussian mixture model, wavelet based & optical flow based method. The purpose of this paper is to do comparative study between background subtraction and modified background subtraction. The moving object detection method have been implemented using MATLAB and results are compared based on completeness of detected object, noise etc.
Moving Object Detection Based on Background Subtraction
Moving object detection is a task to identify the physical motion of an object in a specific region or area. Over the last few years, moving object detection has received much attention due to its wide range of applications like video surveillance, human motion analysis, robot navigation, event detection, anomaly detection, video conferencing, traffic analysis and security. In this paper, a framework is proposed for the evaluation of object detection algorithms in surveillance applications using background subtraction and Mixture of Gaussian. Experimental results show that our technique achieved promising accuracy.
Background modelling and background subtraction performance for object detection
2010 6th International Colloquium on Signal Processing & its Applications, 2010
Moving object detection in video applications is usually performed based on techniques such as background subtraction, optical flow and temporal differencing. The most popular literature technique approach to detect moving object from video sequences is background subtraction. This approach utilized mathematical model of static background and comparing it with every new frame of video sequence. In this paper, background subtraction technique using Mixture of Gaussian (MoG) method is conducted for detection of moving object at outdoor environment. Focus is specified at the five parameters of MoG namely background component weight threshold (T S ), standard deviation scaling factor (D), user-define learning rate (α), Total number of Gaussian components (K) and Maximum number of components M in the background model (M) to give significant impact in producing the optimize background subtraction process. Experimental results showed that by varying each of the parameter can produce acceptable results that enable us to propose suitable parameter range of each parameter for detection of moving object in an outdoor environment.
2018
This paper proposes a novel method for the improvement of basic Background Subtraction (BGS) methods to detect moving objects in video surveillance streams. The method is based on Local Neighborhood Differencing (LND) in which instead of finding a simple pixel to pixel difference between current frame and background model, the average of the pixel neighborhoods from the current frame and background model are subtracted to entitle the pixel a background or foreground in the current frame in order to find moving objects in video. The proposed method has been tested on two basic methods; Adaptive Mean and Adaptive Median methods of object detection using various complex real time benchmarked scenarios. It is also compared with classical statistical thresholding method. The results have been measured in precision and recall metrics to register improvement. The obtained results have confirmed the utility of the method by increasing the robustness of the object detection techniques in vid...
Vehicle detection using background subtraction and clustering algorithms
TELKOMNIKA Telecommunication Computing Electronics and Control, 2019
Traffic congestion has raised worldwide as a result of growing motorization, urbanization, and population. In fact, congestion reduces the efficiency of transportation infrastructure usage and increases travel time, air pollutions as well as fuel consumption. Then, Intelligent Transportation System (ITS) comes as a solution of this problem by implementing information technology and communications networks. One classical option of Intelligent Transportation Systems is video camera technology. Particularly, the video system has been applied to collect traffic data including vehicle detection and analysis. However, this application still has limitation when it has to deal with a complex traffic and environmental condition. Thus, the research proposes OTSU, FCM and K-means methods and their comparison in video image processing. OTSU is a classical algorithm used in image segmentation, which is able to cluster pixels into foreground and background. However, only FCM (Fuzzy C-Means) and K-means algorithms have been successfully applied to cluster pixels without supervision. Therefore, these methods seem to be more potential to generate the MSE values for defining a clearer threshold for background subtraction on a moving object with varying environmental conditions. Comparison of these methods is assessed from MSE and PSNR values. The best MSE result is demonstrated from K-means and a good PSNR is obtained from FCM. Thus, the application of the clustering algorithms in detection of moving objects in various condition is more promising.
Comparing Background Subtraction Algorithms and Method of Car Counting
2012
In this paper, we compare various image background subtraction algorithms with the ground truth of cars counted. We have given a sample of thousand images, which are the snap shots of current traffic as records at various intersections and highways. We have also counted an approximate number of cars that are visible in these images. In order to ascertain the accuracy of algorithms to be used for the processing of million images, we compare them on many metrics that includes (i) Scalability (ii) Accuracy (iii) Processing time.
Evaluation and Improvements of a Real-Time Background Subtraction Method
2005
In a video surveillance system, moving object detection is the most challenging problem especially if the system is applied in complex environments with variable lighting, dynamic and articulate scenes, etc.. Furthermore, a video surveillance system is a real-time application, so discouraging the use of good, but computationally expensive, solutions. This paper presents a set of improvements of a basic background subtraction algorithm that are suitable for video surveillance applications. Besides we present a new evaluation scheme never used in the context of moving object detection algorithms.