A pixel-wise local information-based background subtraction approach

Object detection is an important task for computer vision applications. Many researchers have proposed a number of methods to detect the objects through background modeling. To adapt to “illumination changes” in the background, local feature-based background models are proposed. They assume that local features are not affected by background changes. However, “motion changes”, such as the movement of trees, affect the local features in the background significantly. Therefore, it is difficult for local feature-based models to handle motion changes in the background. To solve this problem, we propose a new background model in this paper by applying a statistical framework to a local feature-based approach. Our proposed method combines the concepts of statistical and local feature-based approaches into a single framework. In particular, we use illumination invariant local features and describe their distribution by Gaussian Mixture Models (GMMs). The local feature has the ability to tolerate the effects of “illumination changes”, and the GMM can learn the variety of “motion changes”. As a result, this method can handle both background changes. Some experimental results show that the proposed method can detect the foreground objects robustly against both illumination changes and motion changes in the background.

This work proposes a fast background learning algorithm for foreground detection under changing illumination. Gaussian Mixture Model (GMM) is an effective statistical model in background learning. We first focus on Titterington's online EM algorithm that can be used for real-time unsupervised GMM learning, and then advocate a deterministic data assignment strategy to avoid Bayesian computation. The color of the foreground is apt to be influenced by the environmental illumination that usually produce undesirable effect for GMM updating, however, a collinear feature of pixel intensity under changing light is discovered in RGB color space. This feature is afterward used as a reliable clue to decide which part of mixture to update under changing light. A foreground detection step proposed in early version of this work is employed to extract foreground objects by comparing the estimated background model with the current video frame. Experiments have shown the proposed method is able to achieve satisfactory static background images of scenes as well as is also superior to some mainstream methods in detection performance under both indoor and outdoor scenes.

Background modeling and subtraction is a natural technique for object detection in videos captured by a static camera, and also a critical preprocessing step in various high-level computer vision applications. However, there have not been many studies concerning useful features and binary segmentation algorithms for this problem. We propose a pixelwise background modeling and subtraction technique using multiple features, where generative and discriminative techniques are combined for classification. In our algorithm, color, gradient, and Haar-like features are integrated to handle spatio-temporal variations for each pixel. A pixelwise generative background model is obtained for each feature efficiently and effectively by Kernel Density Approximation (KDA). Background subtraction is performed in a discriminative manner using a Support Vector Machine (SVM) over background likelihood vectors for a set of features. The proposed algorithm is robust to shadow, illumination changes, spatial variations of background. We compare the performance of the algorithm with other density-based methods using several different feature combinations and modeling techniques, both quantitatively and qualitatively.

In real time object tracking is an important task in various surveillance application. Nowadays, surveillance system very common in offices, collage campus, shopping malls etc. In this paper, an automated video surveillance system presented. Althrough tracking and counting system are commercially available today; there is need for further reaserach to address the challenges of real world scenarios. There is lot of surveillance cameras installed around us but there are no means to monitor all of them continuously. It is necessary to develop a computer vision based technologies that automatically process those images in order to detect situations or unusual behavior. In order to solve the problem of poor real time tracking performance using convolutional neural network. This paper proposes a fast and accurate real time video detection and tracking algorithm. Keyword: - Video surveillance system, Moving object detection, Tracking, Background subtraction. I. Introduction Real time human body detection and tracking in indoor environment deals with the concept of higher level application of image processing and computer vision. The following key components of identify to path of real time tracking are extracting a feature, background image subtraction. If two similar color objects come to close the tracker of one object can jump to associated object. The problem is called hijacking problem. Drifting problem occur when an object abruptly changes its direction to reverse. In that case it’s become very difficult to track the object because the motion model does not work. An object tracking can be understood as the problem of finding the path (i.e. Trajectory).

Ponte Academic JournalNov 2017, Volume 73, Issue 11 EFFICIENT TEXTURE BASED FUZZY DISTANCE CLUSTERING AND SOM OPTIMAL CLASSIFIER FOR FOREGROUND MOVING OBJECT DETECTIONAuthor(s): V. Naga Bushan ,Ch. Satyananda ReddyJ. Ponte - Nov 2017 - Volume 73 - Issue 11 doi: 10.21506/j.ponte.2017.11.10 Abstract:Foreground moving object detection plays a critical role in video surveillance systems. Local Binary Pattern (LBP) based fuzzy distance segmentation is proposed for object foreground detection which uses modified training rate that deals with a change in the foreground of different rates. The extracted features are trained by using hybrid self-organizing map (SOM) based artificial bee colony (ABC) optimization. This approach deals with dynamic background and the illumination changes effectively. In this fuzzy c_means based segmentation effectively segments the moving object for foreground detection. Then, the feature from the foreground is extracted using the LBP feature extraction technique. Thus, foreground image can be further classified for identifying the object under moving using SOM with optimization. The proposed work is simulated in the working platform of Matlab, and the performance is analyzed by comparing with the existing techniques. Experimental results declare that the proposed method accomplishes more changes in the precision over existing techniques. Download full text:Check if you have access through your login credentials or your institution Username Password

Many applications of computer vision, motion captures nowadays are an active research field. Supported by camera innovation in high definition technology and high-speed processing unit technology make higher degree on object detection standard. We can see it from the increasing number of new methods that have improvement in accuracy. In automatic vehicle surveillance area, Spatial Mixture Gaussian model becomes well-known moving based object detection via background subtraction technique in this decades. This method models particular pixel as mixture of Gaussians distribution with regard to pixel's higher probability of occurrences and variance of each Gaussians in the mixture model. Although, this model has threshold to control the sensitivity of object's motion, it has problem with separating an object from its shadow. This is happening because the shadow attaches to the object. Since they always move in tandem, as the result, detected object area will merge and shadow and object will form into a single unity that is difficult to separate. In accordance with detection, occluded object because of a shadow will decrease detector's accuracy. Therefore, we need to remove shadow, in order to maintain detector's quality of accuracy. Challenge in doing so is there is exist dynamic illumination condition which resulting a nonuniform shadow pixel value. This can cause failure of threshold-based linear shadow casting technique. To solve above-mentioned problem, we need a shadow filter that can adapt to the illumination changes. In this experiment, we have successfully implemented an adaptive shadow filter based on DSD algorithm to improve background subtraction method. Our proposed method has a stable result in outdoor environment dataset and it is proven to be able applied to traffic surveillance video application.

Object detection in video surveillance is typically done through background subtraction or temporal differencing. While these techniques perform very well under scenes where there are minimal light changes, they begin to fail when the scene contains rapid illumination changes. The effects of this is most profound in indoor environments. Under these conditions, the background modeling techniques produce large numbers of false positives. This paper proposes a sequential approach to suppressing these false positives. Both frame and region level spatial scales are considered to detect sudden light changes and make use of Gabor filter responses and edge maps to identify and remove false positives.

배경추출은 비디오 감시 시스템에서 움직이는 물체를 찾는데 중요한 기술이다. 본 논문에서는 벡터 정렬을 이용한 새로운 온라인 컬러 배경 추출 방법을 제안한다. 제안된 방법에서 배경은 물체보다 발생빈도가 높다는 사실을 이용하여, 연속된 프레임의 컬러화소 값들의 벡터 미디언을 그 화소에서의 배경이라 간주한다. 본 알고리즘에서 현재 프레임의 물체는 얻어진 배경과의 거리가 문턱치보다 큰 화소들의 집합으로 구성된다. 알고리즘의 성능을 평가하기 위하여 온라인 가우시안 혼합 모델(Gaussian Mixture Model)을 이용한 다중 배경추출 방법과 비교하였으며, 비교결과 유사 또는 우월한 실험 결과를 확인하였다. Background extraction is an important technique to find the moving objects in video surveillance system. This paper proposes a new on-line background extraction method for color video using vector order statistics. In the proposed method, using the fact that background occurs more frequently than objects, the vector median of color pixels in consecutive frames Is treated as background at the position. Also, the objects of current frame are consisted of the set of pixels whose distance from background pixel is larger than threshold. In the paper, the proposed method is compared with the on-line multiple background extraction based on Gaussian mixture model(GMM) in order to evaluate the performance. As the result, its performance is similar or superior to the method based on GMM.

Background subtraction is a very popular approach for vehicle detection in traffic surveillance systems. A conventional color histogram (CCH) considers neither the color similarity across different bins nor the color dissimilarity in the same bin. Therefore, it is sensitive to noisy interference such as illumination changes and quantization errors. Furthermore, CCHs large dimension or histogram bins requires large computation on histogram comparison. However, structured motion patterns of the background (e.g., Vehicular traffic videos, waving leaves, spouting fountain, rippling water, etc.), which are distinctive from variations due to noise, are hardly tolerated in this assumption. To address these concerns, we introduce a background subtraction algorithm for temporally dynamic texture scenes. Specifically, we propose to adopt a clustering-based feature, called fuzzy color histogram (FCH), which has an ability of greatly attenuating color variations generated by background motions while still highlighting moving objects. Experimental results demonstrate that the proposed method is effective for background subtraction in dynamic texture scenes using LFCH features with adaptive updating procedure compared to several competitive methods proposed in the literature.

This paper introduces a novel method to calculate multi-directional projections of squared images, similar to the Radon transformation. Image projections are often used as object signatures for detection, matching and tracking techniques in computer vision. The Radon transformation provides a fast solution to calculate these pixel intensity sums. The proposed method is based on trigonometric functions and basic coordinate-geometry. The solution is implemented sequentially and the runtimes of a GPU-based implementation are measured and evaluated. The analysis of the results indicate that further research is applicable, new parallel models should be discussed.

We derive the decomposition of the anisotropic Gaussian in a one-dimensional (1-D) Gauss filter in the x-direction followed by a 1-D filter in a nonorthogonal direction phi. So also the anisotropic Gaussian can be decomposed by dimension. This appears to be extremely efficient from a computing perspective. An implementation scheme for normal convolution and for recursive filtering is proposed. Also directed derivative filters are demonstrated. For the recursive implementation, filtering an 512 x 512 image is performed within 40 msec on a current state of the art PC, gaining over 3 times in performance for a typical filter, independent of the standard deviations and orientation of the filter. Accuracy of the filters is still reasonable when compared to truncation error or recursive approximation error. The anisotropic Gaussian filtering method allows fast calculation of edge and ridge maps, with high spatial and angular accuracy. For tracking applications, the normal anisotropic convolution scheme is more advantageous, with applications in the detection of dashed lines in engineering drawings. The recursive implementation is more attractive in feature detection applications, for instance in affine invariant edge and ridge detection in computer vision. The proposed computational filtering method enables the practical applicability of orientation scale-space analysis.

Object Detection in Computer Vision: A Comparative Analysis of Advanced Computer Vision Models

In this paper, an educational kit that can be used in AI education was developed to solve the difficulties of AI education. Through this, object detection and person detection in computer vision using CNN and OpenCV to learn practical-oriented experiences from theory-centered and user image recognition (Your Own) that learns and recognizes specific objects Image Recognition), user object classification (Segmentation) and segmentation (Classification Datasets), IoT hardware control that attacks the learned target, and Jetson Nano GPIO, an AI board, are developed and utilized to develop and utilize textbooks that help effective AI learning made it possible.

Box representation has been extensively used for object detection in computer vision. Such representation is efficacious but not necessarily optimized for biomedical objects (e.g., glomeruli), which play an essential role in renal pathology. In this paper, we propose a simple circle representation for medical object detection and introduce CircleNet, an anchor-free detection framework. Compared with the conventional bounding box representation, the proposed bounding circle representation innovates in three-fold: (1) it is optimized for ball-shaped biomedical objects; (2) The circle representation reduced the degree of freedom compared with box representation; (3) It is naturally more rotation invariant. When detecting glomeruli and nuclei on pathological images, the proposed circle representation achieved superior detection performance and be more rotation-invariant, compared with the bounding box. The code has been made publicly available: https://github.com/hrlblab/CircleNet.

The fine state of targets can be represented by the extracted micro-Doppler (m-D) components from the radar echo. However, current methods do not consider the specialty of the m-D components, and their performance with non-sinusoidal components is poor. In this paper, a neural network is applied to signal extraction for the first time. Inspired by the semantic line detection in computer vision, the extraction of the m-D components is transformed into the network-based time–frequency curves detection problem. Specifically, a novel dual-branch network-based m-D components extraction method is proposed. According to the property of intersected multiple m-D components, the dual-branch network consisting of a continuous m-D components extraction branch, and a crossing point detection branch is designed to obtain components and cross points at the same time. In addition, a shuffle attention-fast Fourier convolution (SA-FFC) module is proposed to fuse local and global contexts and focus on key features. To solve the error correlation problem of multi-component signals, the first-order parametric continuous condition and cubic spline interpolation are employed to obtain complete and smooth components curves. Simulation and measurement results show that this method of good robustness is a good candidate for separating the non-sinusoidal m-D components with intersections.