Adaptive BP-RRC mixture model for background subtraction

Dynamic backgrounds and sudden illumination changes are two of the major problems associated with background subtraction techniques. In this paper, we present a novel approach to background subtraction that addresses both of these challenges. In particular, we present an improved codebook background modelling and subtraction technique. We utilise image segmentation on the background image and model the background with a codebook for each pixel along with a pseudo background layer. We perceive background motion as an occlusion of one background layer by a nearby background layer. In other words, sliding of one background layer over a neighbouring layer causes background motion and will hence result in false segmentation. We present our approach of codeword spreading across layer boundaries to handle background motion. Furthermore, we present a two-step update of the background codebook to handle both sudden and gradual illumination changes.

본 논문은 조명의 변화가 심한 영상에서 손 형태를 안정적으로 인지하는 기법에 관한 것이다. 제안한 방법은 HSI 색상공간에서 색상(Hue) 및 색상 기울기(Hue-Gradient)를 기반으로 정의된 배경모델을 구축하고, 실시간으로 입력되는 영상과의 배경차분(background subtraction)기법을 이용하여 배경과 손을 구분한다. 추출된 손의 영역으로부터 18가지의 특징요소를 추출하고 이를 기반으로 다중클래스 SVM(Support Vector Machine) 학습 기법을 사용하여 손의 형태를 인지한다. 제안 기법은 색상 기울기를 배경 차분에 적용함으로써, 조명 환경이 배경 모델의 조명과 다르게 급격한 변화가 이루어졌을 때도 안정적으로 손의 윤곽정보를 추출할 수 있도록 하였다. 또한, 실시간 처리를 저해하는 복잡한 손의 특성정보 대신, OBB의 크기에 대하여 정규화된 두 개의 고유값과 객체 기반 바운딩 박스(OBB)를 구성하는 16개 세부 영역에서의 손 윤곽픽셀의 개수를 손의 특성정보로 사용하였다. 본 논문에서는 급격한 조명 변화 상황에서 기존 RGB 색상요소를 기반으로 하는 배경차분법과 색상을 기반으로 하는 배경차분법, 본 논문에서 제안하는 색상 기울기 기반 배경 차분법의 결과를 비교함으로써 제안 기법의 안정성을 입증하였다. 6명의 실험대상자의 1부터 9까지의 수지화 2700개의 영상으로부터 손 특성 정보를 추출하고 이에 대하여 훈련을 통한 학습 모델을 생성하였다. 학습모델을 기반으로 실험자 6인의 손 형태 1620개의 데이터에 대하여 인지 실험을 실시하여 92.6%에 이르는 손 형태 인식 성공률을 얻었다. In this paper, we present a robust hand recognition approach to sudden illumination changes. The proposed approach constructs a background model with respect to hue and hue gradient in HSI color space and extracts a foreground hand region from an input image using the background subtraction method. Eighteen features are defined for a hand pose and multi-class SVM(Support Vector Machine) approach is applied to learn and classify hand poses based on eighteen features. The proposed approach robustly extracts the contour of a hand with variations in illumination by applying the hue gradient into the background subtraction. A hand pose is defined by two Eigen values which are normalized by the size of OBB(Object-Oriented Bounding Box), and sixteen feature values which represent the number of hand contour points included in each subrange of OBB. We compared the RGB-based background subtraction, hue-based background subtraction and the proposed approach with sudden illumination changes and proved the robustness of the proposed approach. In the experiment, we built a hand pose training model from 2,700 sample hand images of six subjects which represent nine numerical numbers from one to nine. Our implementation result shows 92.6% of successful recognition rate for 1,620 hand images with various lighting condition using the training model.

This paper proposes a robust method to detect and extract silhouettes of foreground objects from a video sequence of a static camera based on the improved background subtraction technique. The proposed method analyses statistically the pixel history as time series observations. The proposed method presents a robust technique to detect motions based on kernel density estimation. Two consecutive stages of the k-means clustering algorithm are utilized to identify the most reliable background regions and decrease the detection of false positives. Pixel and object based updating mechanism for the background model is presented to cope with challenges like gradual and sudden illumination changes, ghost appearance, non-stationary background objects, and moving objects that remain stable for more than the half of the training period. Experimental results show the efficiency and the robustness of the proposed method to detect and extract the silhouettes of moving objects in outdoor and indoor environments compared with conventional methods.

To solve the problem of indoor surveillance, this paper is concerned about the framework that allows the detection of moving objects based on IP camera. The current background subtraction algorithms cannot correctly classify foreground and background if there are sudden illumination changes. To eliminate the influence of illumination changes, intensity flicker correction technique is employed before background subtraction in this paper. Meanwhile to reduce the false alarms further, the union of two separate detection results is employed. In the experimental results, both global illumination changes and local ones are included. The results show that the improved subtractor proposed in this paper performs better than ViBe both in the aspect of correct detection rate and false alarms.

The schooling behavior of both light- and dark-adapted Astyanax mexicanus (Fillipi) was observed under sudden changes in illumination in the range 10−7 to 100 ft-c at different times in the diel cycle. In darkness the fish were dispersed, swam slowly, and showed no signs of mutual recognition of one another. There was no simple relationship between the schooling response and the illumination, but in general the frequency, size, and durability of schools were proportional to the illumination. Schooling was maximal above 10−1 ft-c and approached zero at 10−6 ft-c. The schooling at any level of illumination varied with the testing procedures. The lowest responses were obtained with a gradual reduction of illumination in the evening. At illumination below 10−2 ft-c maximal schooling was obtained from dark-adapted fish. From 10−2 ft-c upward, it was obtained from light-adapted fish. The schooling rate (number of fish in a school) reached 100% for a few seconds at 10−3 ft-c and for longer periods at higher illumination, but was never sustained at the 100% level throughout any 4-min period. Schooling was generally enhanced by any sudden change in illumination but was never sustained at a high rate under monotonous conditions. The decline with monotony was equated with reduction of the fright situation. The decline with reduced illumination was attributed to the reduction of visual acuity and visibility, and occurred at a greater rate among faster-swimming fish. Notemigonus crysoleucas (Mitchill), swimming very slowly, exhibited high schooling rates at 10−5 ft-c. Earlier work showed that the retina of Astyanax in continuous darkness undergoes a rhythmic diel shift. The behavior showed that this retinal shift is not the classical transformation from scotopic to photopic vision. Dark-adapted during the day, the fish demonstrated rapid visual perception under sudden illumination at any value from 10−6 to 10−0.69 ft-c. Dark-adapted fish at night exhibited increasingly longer light-shock reactions with sudden illumination above 10−2 ft-c. According to schooling responses the cone threshold was about 10−3 ft-c, and the rod threshold about 10−6 ft-c. The mesopic range extended from about 10−3 to 10−2 ft-c.

In order to improve the trajectory control effect of multi‐degree‐of‐freedom industrial robots, this paper combines visual image technology to conduct research on trajectory control of multi‐degree‐of‐freedom industrial robots. Aiming at the problem of video segmentation under sudden illumination changes, this paper uses a Gaussian mixture model based on the global illumination function to adopt a variety of illumination invariant features, and proposes a scene segmentation algorithm suitable for sudden illumination changes. Moreover, this paper compares and verifies the algorithm from the subjective and objective perspectives through experiments, which shows that the algorithm in this paper can segment the scene more accurately even in the environment of sudden changes in illumination. In addition, the results of the accuracy test and the trajectory control test show that the research method of the multi‐degree‐of‐freedom industrial robot trajectory control based on the visual image proposed in this paper can effectively improve the trajectory control effect of the robot.

This study presents a hierarchical background modelling and subtraction approach for real‐time detection of moving objects. At the first level, a novel pixel‐wise background modelling method is proposed for coarse detection. The method can dynamically assign the optimal number of components for each pixel with the borrow–lend strategy. And a flexible learning rate which is variable and different for each component is presented to adapt to scene changes. Additionally, a new mechanism using a framework of finite state machine is introduced to maintain and update the background models. At the second level, in order to deal with sudden illumination changes, a block‐wise foreground validation approach is adopted for refined detection. The authors compare the proposed approach with state‐of‐the‐art methods and experimental results under various scenes demonstrate the robustness and effectiveness of the proposed approach.

Developmental changes in behavioral and retinomotor responses of Pacific bluefin tuna on exposure to sudden changes in illumination

This paper presents a moving object detection method against sudden illumination change using improved background modeling. Initially, background model is created for every pixel from the first frame. The sample values for the model of a pixel are collected from the neighborhood of that pixel. Then the new pixel values from the new frames are compared to make background foreground decision. Conventional background modeling faces problem with change in the illumination of the scene. The proposed method frequently checks whether abrupt change of illumination take place or not and then initialize the background model from the frame that is detected with changed illumination. The illumination change is detected by obtaining the images of two frames that are taken at a suitable interval in HSV color space. Then the mean change value of each channel is calculated to make a decision. This enables the background model to start over with new sample values that are obtained in the current illumination condition and the background subtraction process can successfully detect moving object with greater accuracy even in changing illumination condition. Simulation results indicate that the proposed method gives excellent results in illumination changing condition to detect moving object whereas the conventional background modeling can not detect accurately. Comparison analysis shows that our proposed method outperforms recent methods in terms of detection accuracy.

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.

Robust visual tracking is the important stage in the computer vision applications such as robotics, man-free control systems, and the visual surveillance. Accurate motion states estimation and the target representation in visual tracking system are based on the appearances of the target. The factor affects the learning of target representation is the accumulated error due to the pose, illumination changes, and the uneven background. The presence of dynamic background and the shadowing effects causes the visual drift and destructive information. Besides, the misclassification of target region induces the false detection of moving objects. The K-means and Fuzzy-C-means clustering algorithms are available to segment the foreground/background and suppress the shadow region on the basis of the non-changing background of the surveillance area. This paper proposes the novel background normalization technique with textural pattern analysis to suppress the shadow region. The Neighborhood Chain Prediction (NCP) algorithm is used to cluster the uneven background and the Differential Boundary Pattern (DBP) extracts the texture of the video frame to suppress the shadow pixels present in the frame. The lower intensity estimation and the prediction of the area around the lower intensity in proposed work enhance the pixels for shadow removal. The shadow-free frame split up into several grids and the histograms of features are extracted from the grid formatted frame. Finally, the Machine Level Classification (MLC) finds the matching grid corresponds to the tracking region and provides the binary labeling to separate the background and foreground. The proposed DBP-based visual tracking system is high robustness over the sudden illumination changes and the dynamic background due to the texture pattern analysis. The comparison of proposed NCP-DBP combination with the existing segmentation techniques regarding the accuracy, precision, recall, F-measure, success and error rate assured the effectiveness in visual tracking applications.

Foreground segmentation in a video is a key process in many applications such as object detection, an object tracking, and a behavior analysis. Since the extracted foreground objects are often used in the analytical process, the quality of the foreground is a significant factor to the success of these applications. However, there are many key challenges in the foreground segmentation, including dynamic backgrounds, gradual illumination changes, sudden illumination changes, shadows, and long-term scene changes. This paper proposes a novel dynamic codebook method to address such challenges. The dynamic codebook aims to significantly improve the conventional well-known codebook technique by introducing a technique to make a dynamic boundary of each codeword. In this technique, the lab color space is used in order to make the model more resilient to the illumination change. The experimental results and comprehensive comparisons demonstrate that the proposed method can achieve very promising performance.

The analysis of moving objects in video sequences has been a paramount issue in applications related to intelligent surveillance systems, robotics, and medicine. Although several works aimed to analyze objects in video sequences have been reported, many of them need manual parameter adjustments and they are not tolerant to illumination changes and dynamic backgrounds. Therefore, a novel scheme termed Dynamic Retinotopic SOM based on an adaptive artificial neural network, to detect moving objects is proposed in this work. The neural network is a model based on the mechanisms of the visual cortex that we called Retinotopic SOM (RESOM) and it is also proposed in this paper. Furthermore, RESOM is a real-time neural network that can adapt its learning parameters based on the scene behavior and it mimics perception abilities. A quantitative comparison with other segmentation methods reported in the literature using real video scenes showed that the proposed DR-SOM segmentation method automatically adjusts its parameters and outperforms the reported methods in condition of dynamic backgrounds, and gradual and sudden illumination changes.

In this paper, we propose a novel image segmentation algorithm for license plate recognition (LPR) in video based traffic surveillance system. The license plate character segmentation is most important procedure in LPR system. However, in real situation, the character segmentation algorithms are challenged by drastic performance decrease due to sudden local illumination changes, especially when the color of characters is similar to that of background in LP. To mitigate this problem, we introduce a novel LP character segmentation algorithm by employing an adaptive binarization method using super-pixel based degeneracy factor. The proposed method demonstrates a significant improvement over conventional methods.

Background subtraction is an important part of various computer vision applications that can detect the foreground objects by comparing the current pixels with a background model. The general approaches gradually update the background model according to the current status, but might fail in sudden illumination changes. An illumination‐robust background modelling method is proposed to address this problem. The method provides quick illumination compensation using two background models with different adaption rates. Experimental results show that the proposed algorithm outperforms several state‐of‐art approaches and provides low computational cost.