An improved background and foreground modeling using kernel density estimation in moving object detection
9
Citation
12
Reference
10
Related Paper
Citation Trend
Abstract:
For the purpose of precisely distinguishing the true moving target and the background in video surveillance, many strategies based on both background and foreground modeling have been proposed recent years. In this paper, we presented an improved moving object detection algorithm based on kernel density estimation which has two features. First, we construct a novel background and foreground model based on the basic nonparametric kernel density estimation and a joint domain-range foreground model. The foreground model applied here assures a more accurate detection result especially with dynamic backgrounds and building background with basic kernel density estimation helps to reduce the amount of computational cost which is usually a large number in many of the exists background-foreground models. Second, we present a strategy using edge detection to adaptively updating the background. By taking this method, our algorithm carrying out a quite exactly detecting result while immediately adjust to the changes in the background model, such like illumination change, objects from movement to static or conversely. Experimental results show that our proposal efficiently suppressed the inaccuracy caused by multiple reasons.Keywords:
Kernel density estimation
Foreground detection
Kernel (algebra)
Density estimation
Background modeling has emerged as a popular foreground detection technique for various applications in video surveillance. Background modeling methods have become increasing efficient in robustly modeling the background and hence detecting moving objects in any visual scene. Although several background subtraction and foreground detection have been proposed recently, no traditional algorithm today still seem to be able to simultaneously address all the key challenges of illumination variation, dynamic camera motion, cluttered background and occlusion. This limitation can be attributed to the lack of systematic investigation concerning the role and importance of features within background modeling and foreground detection. With the availability of a rather large set of invariant features, the challenge is in determining the best combination of features that would improve accuracy and robustness in detection. The purpose of this study is to initiate a rigorous and comprehensive survey of features used within background modeling and foreground detection. Further, this paper presents a systematic experimental and statistical analysis of techniques that provide valuable insight on the trends in background modeling and use it to draw meaningful recommendations for practitioners. In this paper, a preliminary review of the key characteristics of features based on the types and sizes is provided in addition to investigating their intrinsic spectral, spatial and temporal properties. Furthermore, improvements using statistical and fuzzy tools are examined and techniques based on multiple features are benchmarked against reliability and selection criterion. Finally, a description of the different resources available such as datasets and codes is provided.
Foreground detection
Robustness
Cite
Citations (8)
Background subtraction techniques, in order to identify moving objects, are commonly used in computer vision applications. This is still a challenging problem especially when there is a non-stationary background. Two most significant tasks of generic background subtraction techniques are the background modeling, which determines how background will be represented and detect the foreground region, which significantly differ from the background model. The problem with which features will be represented in both background modeling and foreground detection is an important research topic. The purpose of this study is to determine the most effective color space, region block size and distance metric for foreground detection regardless of the background model. As a performance metric, the area under the Receiver Operating Characteristic (ROC) curve is used. Tests were performed over 9 different videos which have non-static background in I2R dataset.
Foreground detection
Background image
Background color
Cite
Citations (5)
Foreground detection
Background image
Similarity (geometry)
Subtraction
Sequence (biology)
Image subtraction
Cite
Citations (5)
This paper addresses the problem of foreground detection in realtime video surveillance applications.We propose a framework, which is computationally cheap and has low memory requirements.It combines two simple processing blocks, both of which are essentially background subtraction algorithms.The main novelty of our approach is a combination of autoregressive moving average filter with two background models having different adaptation speeds.The first model, having a lower adaptation speed, models long-term background and detects foreground objects by finding areas in current frame which significantly differ from the proposed background model.The second model, with a higher adaptation speed, models the short-term background and is responsible for finding regions in the scene with a high foreground object activity.Our final foreground detection is built by combining the outputs from these building blocks.The foreground obtained by the long-term modeling block is verified by the output of the short-term modeling block, i.e.only the objects exhibiting significant motion are detected as a real foreground objects.The proposed method results in a very good foreground detection performance at a low computational cost.
Foreground detection
Novelty Detection
Motion Detection
Cite
Citations (8)
Background subtraction is a real time effective technique for detecting moving foreground objects in image sequences from a static camera. Background modelling plays an important role in this technique of foreground object detection. Active real time background modelling in presence of moving foreground objects in the scene and adaption of background model to gradual changes due to gradual illumination changes and addition of new immoveable objects into the scene are addressed in this paper. We present a queue based algorithm for real time, active, and adaptive background modelling. Segmentation of the foreground and robust detection of shadow is performed via comparison with background statistics in YCrCb color space. The problem of a single foreground object splitting into two or more segments due to similarity of foreground pixel color with the background in most cases can be ameliorated with the use of a fast single pass the hysteresis thresholding technique. We demonstrate various results of background modelling, segmentation and shadow detection results for both indoor and outdoor scenes.
Foreground detection
Similarity (geometry)
Cite
Citations (23)
Real-time segmentation of scene into foreground and background is an important issue for many applications. Different from previous codebook (CB) methods, this paper introduces a hybrid CB model by combining the mixture of Gaussian (MOG) method and the CB method. It can be used to solve the problems of moving background and shadow/highlight on the background and background. Our method avoids extracting false foreground pixels or missing real foreground pixels. The experimental results illustrate the advantages of our method over the other methods.
Foreground detection
Background image
Cite
Citations (4)
Background modeling has emerged as a popular foreground detection technique for various applications in video surveillance. Background modeling methods have become increasing efficient in robustly modeling the background and hence detecting moving objects in any visual scene. Although several background subtraction and foreground detection have been proposed recently, no traditional algorithm today still seem to be able to simultaneously address all the key challenges of illumination variation, dynamic camera motion, cluttered background and occlusion. This limitation can be attributed to the lack of systematic investigation concerning the role and importance of features within background modeling and foreground detection. With the availability of a rather large set of invariant features, the challenge is in determining the best combination of features that would improve accuracy and robustness in detection. The purpose of this study is to initiate a rigorous and comprehensive survey of features used within background modeling and foreground detection. Further, this paper presents a systematic experimental and statistical analysis of techniques that provide valuable insight on the trends in background modeling and use it to draw meaningful recommendations for practitioners. In this paper, a preliminary review of the key characteristics of features based on the types and sizes is provided in addition to investigating their intrinsic spectral, spatial and temporal properties. Furthermore, improvements using statistical and fuzzy tools are examined and techniques based on multiple features are benchmarked against reliability and selection criterion. Finally, a description of the different resources available such as datasets and codes is provided.
Foreground detection
Robustness
Cite
Citations (11)
This paper presents a robust approach for detecting moving objects from a static background scene that contains slow illumination changes, physical changes and micro- movements. First, we propose a new algorithm for background modeling that adapts to slow illumination and physical changes. This algorithm which is based on pixel state computation and background pixel state decision does not need such training sequences excluding moving objects. Second, we develop an efficient background subtraction algorithm that is able to cope with micro-movement of the background scene. This is done by calculating the similarity between the incoming pixel and its neighborhood pixels in the background model. Finally, we applied this robust approach to some video surveillance sequences of both indoor and outdoor scenes. The results demonstrate the effectiveness of our approach.
Foreground detection
Background image
Robustness
Cite
Citations (1)
Chaste and stationary foreground may occur in traditional background subtraction when objects start or stop moving. Eliminating ghosts and extracting stationary foreground immediately are crucial for improving the subsequent tasks such as object tracking, recognition and activity analysis. In this paper, we propose a method to detect ghosts and stationary foreground by dual-direction background modeling. The forward background model and the backward background model are built by GMM and a simple regression model respectively, which can detect not only the moving foreground but also the stationary foreground and the ghosts. Extensive experiment results demonstrate that the proposed algorithm is effective and efficient in eliminating ghosts and detecting stationary foreground.
Foreground detection
Tracking (education)
Cite
Citations (1)
Background modelling is an empirical part in the procedure of foreground mining of idle and moving objects. The foreground object detection has become a challenging phenomenon due to intermittent objects, intensity variation, image artefact and dynamic background in the video analysis and video surveillance applications. In the video surveillances application, a large amount of data is getting processed by everyday basis. Thus it needs an efficient background modelling technique which could process those larger sets of data which promotes effective foreground detection. In this paper, we presented a renewed background modelling method for foreground segmentation. The main objective of the work is to perform the foreground extraction only inthe intended region of interest using proposed Q-Tree algorithm. At most all the present techniques consider their updates to the pixels of the entire frame which may result in inefficient foreground detection with a quick update to slow moving objects. The proposed method contract these defect by extracting the foreground object by controlling the region of interest (the region only where the background subtraction is to be performed) and thereby reducing the false positive and false negative. The extensive experimental results and the evaluation parameters of the proposed approach with the state of art method were compared against the most recent background subtraction approaches. Moreover, we use challenge change detection dataset and the efficiency of our method is analyzed in different environmental conditions (indoor, outdoor) from the CDnet2014 dataset and additional real time videos. The experimental results were satisfactorily verified the strengths and weakness of proposed method against the existing state-of-the-art background modelling methods.
Foreground detection
Tree (set theory)
Cite
Citations (1)