A similarity-window based background estimation approach

Background estimation can be regarded as a problem to construct the background from a series of video frames including moving objects in the scene. Scene background estimation is the essential prerequisite, or at least can be helpful for many applications such as video surveillance, video segmentation, and privacy protection for videos. To perform this task, in this paper we propose a robust framework for scene background modelling based on temporal median filter with Gaussian filtering. Specifically, each background pixel is firstly modeled with a probability density function (PDF) learned over a series of video sequence. Then, pixels in video sequence with low probabilities are filtered, taken as foreground moving objects or noises. Finally, a temporal median filter is employed on video sequence, with pixels left. The performance of our framework is evaluated visually and numerically using different metrics on the scene background modeling contest 2016(SBMnet 2016). Results on the SBMnet dataset indicate that the proposed scheme can improve performance, in comparison with the classic temporal median filter to some extend, for background motion and clutter.

Background subtraction method is widely used in most of the video motion detection algorithms especially for video surveillance application. Background subtraction is used to extract moving or static foreground objects from the background scene. The efficiency of foreground-background segmentation heavily relies on background model which must be able to cope with changes in the scene and granularity of the foreground objects. The robust background model can produce good foreground segmentation results and it is still a great challenge to get accurate and high performance result today. In this paper, a video foreground-background segmentation approach is proposed. This approach is based on Codebook (CB) model with Kalman Filter. This approach can be used to extract foreground objects from the video stream. The Lab color space is used in this approach to calculate color difference between two pixels using CIEDE2000 color difference formula. Extracted foreground object from video sequence using this approach is useful for object detection in video surveillance applications. This approach has been tested with PETS and CDnet2014 datasets and segmentation results accuracy are evaluated compare with ground truth.

Detection of video objects under bad weather and poor illumination condition is a challenging task. We address this issue using the notion of background modeling. LBP-based background modeling and model learning has been used to detect the video objects but performance degrades in the above complex background. We propose the adjacency- and reinforced adjacency-based variants of LBP for complex real-world background modeling and model learning for object detection. In this regard, we have proposed the four variants; (1) enhanced adjacent local binary pattern, (2) enhanced reinforced adjacent local binary pattern, (3) enhanced adjacent local ternary pattern, and (4) enhanced reinforced adjacent local ternary pattern. Besides, we have embedded the Gabor and LBP features to obtain an embedded feature, which is subsequently used with the notions of adjacency. Unlike the background learning approach where the model learns only the background, our model learning algorithm learns the background together with the foreground objects and hence named as mixed-mode learning strategy. These models together with the new learning strategy are tested with CD2014 (snowfall and blizzard) and PETS (2014 and 2016) data sets, and the performance of the proposed models has been compared with LBP-based methods and other state-of-the-art methods.

In this paper, an algorithm is introduced combining a template matching technique with background subtraction to detect moving targets on a road captured on video using a static camera. The designed algorithm based on combining the two techniques is to reduce the difficulties in background estimation and improve the processing time, etc. The proposed algorithm was tested in two different video sequences. The first video sequence was filmed with stabilized camera, while the second video was acquired with unstable camera. The results in both cases indicated that the background subtraction algorithm is successful in detecting moving objects and the template matching technique is suitable for object replacement from background frame to acquire the standard background frame.

This paper presents a fast method for detecting textured objects moving in a video sequence. It is based on a known background estimation and a fixed camera position. The algorithm is able to detect the presence of moving objects and locates them on-line. It is a frame-by-frame method. First, a difference image is computed from the background and the current frame. This yields three classes of pixels, those for which something changed with respect to the background, those for which nothing changed, and finally the pixels for which no decision can be made. Then an a contrario model allows an automatic clustering, by using adapted rectangular windows, of the pixels for which changes have been detected. If necessary, these regions are corrected in order to better fit the moving objects' boundaries. Experimental results show that the algorithm is very robust with respect to noise and to the quality of the background estimation. The choice of the model parameters is quite natural and user friendly. The algorithm has been successfully tested on video sequences coming from different databases, including indoor and outdoor sequences.

A method is presented for extracting object information from an image sequence taken by a static monocular camera. The method was developed towards a low computational complexity in order to be used in real-time surveillance applications. Our approach makes use of both intensity and edge information of each frame and works efficiently in an indoor environment. It consists of two major parts: background processing and foreground extraction. The background estimation and updating makes the object detection robust to environment changes like illumination changes and camera jitter. The fusion of intensity and edge information allows a more precise estimation of the position of the different foreground objects in a video sequence. The result obtained are quite reliable, under a variety of environmental conditions.

In this paper, we introduce a new method to reliably ex- tract humans from a video sequence even when the humans are static for long periods of time. The proposed method addresses a common problem in background subtraction techniques whereby humans that are static are mistaken for new additions to the background scene and are consequently absorbed into the background model. In the proposed method, codebook background subtraction is used to identify foreground regions in the video frame. A motion based particle filter is then used to track one or more human heads in the frame and determine which of these foreground regions represent people. The background model is then selectively updated given this knowledge thus ensuring that people will never be absorbed into the background model once detected, even when indefinitely static. Simulation results confirm that a human body is robustly extracted using this method in a non-static environment. (5 pages)

Background initialization is the task of computing a background model by processing a set of preliminary frames in a video scene. The initial background estimation serves as bootstrap model for video segmentation of foreground objects, although the background estimation could be refined and updated in steady state operation of video processing systems. In this paper we approach the background modeling problem with a weightless neural network called WiSARD $$^{rp}$$ . The proposed approach is straightforward, since the computation is pixel–based and it exploits a dedicated neural network to model the pixel background by using the same training rule.

The background modeling is the very first and essential part of every computer assisted surveillance system. Without it there would be no reliable way for fast and robust detection of moving objects in video sequences. In this paper we collect, describe and compare the main features of the most commonly used techniques of background modeling in video sequences and determine the most desirable way for the development of new algorithms in this field.

Abstract— The positive impact of video and image processing in traffic monitoring, analysis, and traffic condition monitoring in various cities and urban vicinities cannot be overstated. This paper is another approach considering estimating vehicles' speed. Basically, traffic videos are collected from a stationary camera mounted on freeways. On the one hand, camera calibration with respect to accurate measure could mainly solve the geometrical equations conveniently supported directly using references. Camera calibration could yield precise measurements, but rather difficult in achieving the accurate speed. The system is also adaptable to extend into other traffic-related applications. The average errors in detected vehicle speed were ± 7 km/h, and the experiments were conducted under various resolutions and different video sequences. The accelerating advancement of computing power of commonplace computers has now enabled a broader reach in the integration of deep learning techniques in traffic surveillance video comparisons. It constitutes the core functions of traffic analysis, such as prediction of traffic flows, anomaly detection, vehicle re-identification, and vehicle tracking. Among these applications, traffic flow prediction, or vehicle speed estimation, is one of the most serious research subjects during the recent years. It can be a good solution to this problem in preventing road accidents and enhancing road engineering by almost checking the traffic demand. Vehicle speed prediction is effectively proposed through integration of state-of-the-art deep learning models and classical computer vision techniques. Some present-and-state-of-the-art efforts concerning estimation of vehicle speeds, detection of vehicles, and tracking of objects are discussed in this article. Optical flow contains the speed and direction information of pixel displacements in an image. Our final approach deals with collecting multi-scale convolutional network. This extracts parameter data of vehicles. Keywords: Speed detection in vehicles, video sequences, computer vision, background modeling, traffic monitoring, OpenCV

Automated traffic surveillance systems are widely used in intelligent transportation systems (ITS). However, the accuracy of video-based Vehicle Detection is heavily affected by complex environmental factors such as shadows, rain, illumination and glare. This paper introduces an approach to motion vehicle detection in complex condition over highway surveillance video. The framework is composed of two parts: background estimation and multi-feature extraction. A fast constrained Delaunay triangulation (CDT) algorithm based on constrained-edge priority is presented instead of complicated segmentation algorithms. We present a background block update modeling theory based on triangulation to estimate background self-adaptively. Consequently, we can get the difference between the current frame and the background model. After extracting features in triangular candidates, multi-feature eigenvector is created for each vehicle with Principal Component Analysis (PCA). We design a classifier to classify triangular candidate as a part of a real vehicle or not by Support Vector Machine (SVM). And then, a parallelogram is used to represent the vehicle's shape robustly. Finally, experiments using real video sequence are performed to verify the method proposed for complex environmental factors.

The key of background subtraction which is widely used in moving object detecting is to set up and update the background model. This paper presents a block background subtraction method based on ViBe, using the spatial correlation and time continuity of the video sequence. Set up the video sequence background model firstly. Then, update the background model through block processing. Finally employ the difference between the current frame and background model to extract moving objects.

Real-time segmentation of moving objects in video sequences is a fundamental step for surveillance systems. One of successful methods for complex background is to use a multicolor background model per pixel. However, Common problem for this approach is that it suffers from illumination changing environment, in addition, it is incapable of removing shadows of moving objects. This paper proposed an effective scheme to improve the adaptive background model for each pixel by introducing a background training parameter into every Gaussian model, and region-based scheme is applied to judgment by utilizing both spatial and temporal information. Experimental results will be presented to validate proposed algorithm keep robustness in the situation of illumination changes, shadow can be removed in foreground mask, results shows False Alarm Rate can be reduced from 34.9% to 35.8% while the overlap varies within normal range from 0.4 to 0.6 compared with conventional Gaussian mixture model.

A new background estimation and suppression algorithm was presented. In the algorithm, targets and observing noises were considered as mixed interferences of the image background. With this situation, image background was estimated adaptively and then background suppression was done in order to improve the signal-to-noise ratio (SNR) of targets. In this algorithm, firstly, a Zernike-facet model of image background was built up. Secondly, the total least squares (TLS) method was used to solve parameters of the model. Finally, background estimation and suppression were done using the model and its parameters. Simulations and several experiments demonstrating the effectiveness of this proposed algorithm were reported. And results show that this algorithm can be effective to estimate background in mixed noise environment and can preserve detail information of targets and improve SNR of targets. As a result, detecting probability and false probability will be improved in next process for automatic target detection and tracking.

The ViBe algorithm is a powerful technique for the background detection and subtraction in video sequences. Compared to the state-of-the-art algorithms, ViBe algorithm is better in fast speed and less memory consumption. However, when applying ViBe algorithm to the moving objects appeared in the first frame of videos, all pixels in first frame will be used to build the background model that will result in the foreground pixels in sample set. This problem causes the ghost areas emerge. And it will remain for a long time. In this paper, a salient region detection based ViBe algorithm is proposed to eliminate the ghost areas fast. First, the foreground region is extracted from the first frame of videos using the salient region detection algorithm. According to the result of salient region detection, the background area of image is separated from the foreground area. The foreground pixels are dislodged from the sample set. Then, only background pixels are used for background model initialization. The experimental result shows that the improved algorithm can eliminate ghost in few frames quickly.