Moving Objects Segmentation (MOS) is a crucial step in various computer vision applications, such as visual object tracking, autonomous vehicles, human activity analysis, surveillance, and security. ...Existing MOS approaches suffer from performance degradation due to extreme challenging conditions in real world complex environments such as varying illumination conditions, camouflage objects, dynamic backgrounds, shadows, bad weathers and camera jitters. To address these problems we proposed a novel generative adversarial based framework for moving objects segmentation. Our framework works with one classifier discriminator, one representation learning network and one generator jointly trained to perform MOS in various challenging scenarios. During training the discriminator network acts as a decision maker between real and fake training samples using conditional least squares loss. While the representation learning network provides the difference between the deep features of real and fake training samples using content loss formulation. Another loss term we have exploited to train our generator network is the reconstruction loss that minimizes the difference between the spatial information of real and fake training samples. Moreover, we also propose a novel modified U-net architecture for our generator network showing improved performance over Vanilla U-net model. Experimental evaluations of our proposed method on four benchmark datasets in comparison with thirty-two existing methods has demonstrated the strength of our proposed model.
Background Modelling from a Moving Camera Viswanath, Amitha; Behera, Reena Kumari; Senthamilarasu, Vinuchackravarthy ...
Procedia computer science,
2015, 2015-00-00, Letnik:
58
Journal Article
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In video analytics based systems, an efficient method for segmenting foreground objects from video frames is the need of the hour. Currently, foreground segmentation is performed by modelling ...background of the scene with statistical estimates and comparing them with the current scene. Such methods are not applicable for modelling the background of a moving scene, since the background changes between the scenes. The scope of this paper includes solving the problem of background modelling for applications involving moving camera. The proposed method is a non-panoramic background modelling technique that models each pixel with a single Spatio-Temporal Gaussian. Experimentation on various videos promises that the proposed method can detect foreground objects from the frames of moving camera with negligible falsealarms.
Video saliency has a profound effect on our lives with its compression efficiency and precision. There have been several types of research done on image saliency but not on video saliency. This paper ...proposes a modified high efficiency video coding (HEVC) algorithm with background modelling and the implication of classification into coding blocks. This solution first employs the G-picture in the fourth frame as a long-term reference and then it is quantized based on the algorithm that segregates using the background features of the image. Then coding blocks are introduced to decrease the complexity of the HEVC code, reduce time consumption and overall speed up the process of saliency. The solution is experimented upon with the dynamic human fixation 1K (DHF1K) dataset and compared with several other state-of-the-art saliency methods to showcase the reliability and efficiency of the proposed solution.
Robust background modelling in DIALS Parkhurst, James M.; Winter, Graeme; Waterman, David G. ...
Journal of applied crystallography,
December 2016, Letnik:
49, Številka:
6
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A method for estimating the background under each reflection during integration that is robust in the presence of pixel outliers is presented. The method uses a generalized linear model approach that ...is more appropriate for use with Poisson distributed data than traditional approaches to pixel outlier handling in integration programs. The algorithm is most applicable to data with a very low background level where assumptions of a normal distribution are no longer valid as an approximation to the Poisson distribution. It is shown that traditional methods can result in the systematic underestimation of background values. This then results in the reflection intensities being overestimated and gives rise to a change in the overall distribution of reflection intensities in a dataset such that too few weak reflections appear to be recorded. Statistical tests performed during data reduction may mistakenly attribute this to merohedral twinning in the crystal. Application of the robust generalized linear model algorithm is shown to correct for this bias.
The application of a robust generalized linear model framework for the modelling of reflection backgrounds in X‐ray diffraction images is described.
As one of the internet of things (IoT) use cases, wireless surveillance systems are rapidly gaining popularity due to their easier deployability and improved performance. Videos captured by ...surveillance cameras are required to be uploaded for further storage and analysis, while the large amount of its raw data brings great challenges to the transmission through resource-constraint wireless networks. Observing that most collected consecutive frames are redundant with few objects of interest (OoIs), the filtering of these frames before uploading can dramatically relieve the transmission pressure. Additionally, real-world monitoring environment may bring shielding or blind areas in videos, which notoriously affects the accuracy on frame filtering. The collaboration between neighbouring cameras can compensate for such accuracy loss.
Under the computational constraint of edge cameras, we present an efficient video pre-processing strategy for wireless surveillance systems using light-weight AI and IoT collaboration. Two main modules are designed for either fixed or rotated cameras: (i) frame filtering module by dynamic background modelling and light-weight deep learning analysis; and (ii) collaborative validation module for error compensation among neighbouring cameras. Evaluations based on real-collected videos show the efficiency of this strategy. It achieves 64.4% bandwidth saving for the static scenario and 61.1% for the dynamic scenario, compared with the raw video transmission. Remarkably, the relatively high balance ratio between frame filtering accuracy and latency overhead outperforms than state-of-the-art light-weight AI structures and other surveillance video processing methods, implying the feasibility of this strategy.
•Most of foreground detection methods are pixel-based which implies higher complexity.•Several effective methods hardly reach real time execution for 320 × 240 pixels videos.•The rise of low cost ...devices requires lighter foreground detection algorithms.•Each scene has been represented with a set of tilings.•The final foreground mask consists of a combination of the masks of this set.
In this work a novel region-based approach for the detection of foreground in video sequences is presented. The model consists of an ensemble of layers or tilings, where each tiling represents, by means of randomly chosen parallelogram regions, the background of the scene. Currently, the image size of video surveillance cameras far exceeds one megapixel (more than 1024 × 768), and pixel-based proposals are poorly suited for near real-time ratios. Therefore, the analysis by pixel is replaced by an analysis by region, improving the final resolution by overlapping regions or parallelograms with different shapes and sizes. Thus, for each frame, each region estimates the probability of belonging to the foreground or background, to finally compute the consensus foreground mask among all the tilings. With this proposal, it is possible to detect the foreground in high resolution sequences, a process that is not feasible using pixel-level techniques. Several experiments have been carried out by employing a wide range of videos. A qualitative and quantitative comparison with the state-of-the-art algorithms is performed by using a well-known video dataset benchmark. The results show the feasibility of our proposal compared with higher resolution methods.
The research follows the mainstream physics and network system architecture. Aiming at the problem of poor data processing ability and poor robustness of traditional trajectory detection algorithms, ...a trajectory detection method that can be accurately extracted from the fuzzy video of a locomotive is proposed. Firstly, in order to ensure the accuracy of rail detection of trains in complex environments and improve the safety of driverless driving, the video image captured by on-board camera is stored as RGB video frame set, and then processed as single-channel greyscale image carrier set; Secondly, after the initial colour and brightness treatment, the redundant and useless noise features in the greyscale image carrier set still exist. After secondary Gaussian filtering and de-noising, canny operator is used to detect the track edge details of interest; Finally, the rail area is taken as the interested target for Hough line detection, the background subtraction method of adaptive mixed Gaussian background modelling is introduced, the structure element function and the morphologyEx theory of morphological transformation function are introduced, and the left and right tracks are fitted after the calculation and judgement of pixel coordinates. Algorithm for visual tracking experiments show that, rail detection algorithm has already meet need to detect rails in low-quality videos recorded by the on-board cameras of different models of trains at different speed. It not only can process large quantity of data from the on-board camera videos in real time, but also can accurately detect the target rails adaptively where rail conditions are complex with obstructive objects, which shows that this algorithm has very robust performance.
Purpose
Background modelling has played an imperative role in the moving object detection as the progress of foreground extraction during video analysis and surveillance in many real-time ...applications. It is usually done by background subtraction. This method is uprightly based on a mathematical model with a fixed feature as a static background, where the background image is fixed with the foreground object running over it. Usually, this image is taken as the background model and is compared against every new frame of the input video sequence. In this paper, the authors presented a renewed background modelling method for foreground segmentation. The principal objective of the work is to perform the foreground object detection only in the premeditated region of interest (ROI). The ROI is calculated using the proposed algorithm reducing and raising by half (RRH). In this algorithm, the coordinate of a circle with the frame width as the diameter is considered for traversal to find the pixel difference. The change in the pixel intensity is considered to be the foreground object and the position of it is determined based on the pixel location. Most of the techniques study their updates to the pixels of the complete frame which may result in increased false rate; The proposed system deals these flaw by controlling the ROI object (the region only where the background subtraction is performed) and thus extracts a correct foreground by exactly categorizes the pixel as the foreground and mines the precise foreground object. The broad experimental results and the evaluation parameters of the proposed approach with the state of art methods were compared against the most recent background subtraction approaches. Moreover, the efficiency of the authors’ method is analyzed in different situations to prove that this method is available for real-time videos as well as videos available in the 2014 challenge change detection data set.
Design/methodology/approach
In this paper, the authors presented a fresh background modelling method for foreground segmentation. The main objective of the work is to perform the foreground object detection only on the premeditated ROI. The region for foreground extraction is calculated using proposed RRH algorithm. Most of the techniques study their updates to the pixels of the complete frame which may result in increased false rate; most challenging case is that, the slow moving object is updated quickly to detect the foreground region. The anticipated system deals these flaw by controlling the ROI object (the region only where the background subtraction is performed) and thus extracts a correct foreground by exactly categorizing the pixel as the foreground and mining the precise foreground object.
Findings
Plum Analytics provide a new conduit for documenting and contextualizing the public impact and reach of research within digitally networked environments. While limitations are notable, the metrics promoted through the platform can be used to build a more comprehensive view of research impact.
Originality/value
The algorithm used in the work was proposed by the authors and are used for experimental evaluations.
Principal Component Analysis (PCA) has been of great interest in computer vision and pattern recognition. In particular, incrementally learning a PCA model, which is computationally efficient for ...large-scale problems as well as adaptable to reflect the variable state of a dynamic system, is an attractive research topic with numerous applications such as adaptive background modelling and active object recognition. In addition, the conventional PCA, in the sense of least mean squared error minimisation, is susceptible to outlying measurements. To address these two important issues, we present a novel algorithm of incremental PCA, and then extend it to robust PCA. Compared with the previous studies on robust PCA, our algorithm is computationally more efficient. We demonstrate the performance of these algorithms with experimental results on dynamic background modelling and multi-view face modelling.
In order to meet the requirements of background change, illumination variation, moving shadow interference and high accuracy in object detection of moving camera, and strive for real-time and high ...efficiency, this paper presents an object detection algorithm based on sparse approximation recursion and sparse coding migration in subspace. First, low-rank sparse decomposition is used to reduce the dimension of the data. Combining with dictionary sparse representation, the computational model is established by the recursive formula of sparse approximation with the video sequences taken as subspace sets. And the moving object is calculated by the background difference method, which effectively reduces the computational complexity and running time. According to the idea of sparse coding migration, the above operations are carried out in the down-sampling space to further reduce the requirements of computational complexity and memory storage, and this will be adapt to multi-scale target objects and overcome the impact of large anomaly areas. Finally, experiments are carried out on VDAO datasets containing 59 sets of videos. The experimental results show that the algorithm can detect moving object effectively in the moving camera with uniform speed, not only in terms of low computational complexity but also in terms of low storage requirements, so that our proposed algorithm is suitable for detection systems with high real-time requirements.
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