An advanced and user-friendly tool for fast labeling of moving objects captured with surveillance sensors is proposed, which is available to the public. This tool allows the creation of three kinds of labels: moving objects, shadows and occlusions. These labels are created at both the pixel level and object level, which makes them suitable to assess the quality of both moving object detection strategies and tracking algorithms. The labeling can be performed easily and quickly thanks to a very friendly graphical user interface that allows one to automatize many common operations. This interface also includes some semiautomatic advanced tools that simplify the labeling tasks and drastically reduce the time required to obtain high-quality results.

Tool for Semiautomatic Labeling of Moving Objects in Video Sequences: TSLAB

Moving object detection becomes the important task in the video surveilance system. Defining the threshold automatically is challenging to differentiate the moving object from the background within a video. This study proposes gaussian mixture model (GMM) as a threshold strategy in moving object detection. The performance of the proposed method is compared to the Otsu algorithm and gray threshold as the baseline method using mean square error (MSE) and Peak Signal Noise Ratio (PSNR). The performance comparison of the methods is evaluated on human video dataset. The average result of MSE value GMM is 257.18, Otsu is 595.36 and Gray is 645.39, so the MSE value is lower than Otsu and Gray threshold. The average result of PSNR value GMM is 24.71, Otsu is 20.66 and Gray is 19.35, so the PSNR value is higher than Otsu and Gray threshold. The performance of the proposed method outperforms the baseline method in term of error detection.

Adaptive threshold for moving objects detection using gaussian mixture model

Motion segmentation plays a central role in a variety of computer vision and pattern recognition applications. In moving camera videos, the dynamic background caused by camera motion and the object motions caused by multiple moving objects are mixed and dependent with each other making the segmentation of camera motion and object motions quite difficult. This is the known dependent motion segmentation. Although motion segmentation in static scenes is a relative easy work, the dependent motion segmentation in dynamic scenes is very challenging due to the high mixture and dependence between camera motion and object motions. Recently, a large number of works have been developed for the task of dependent motion segmentation. However, their performances are still far behind human perceptions. In this paper, we studied, analyzed, and reviewed the most important and recent developed dependent motion segmentation techniques and proposed a classification strategy to categorize these techniques into different groups according to their characteristics and features. Furthermore, we also discussed the advantages and disadvantages of each kind of techniques and provided suggestions for further research directions.

Dependent Motion Segmentation in Moving Camera Videos: A Survey

It is difficult to track the moving vehicle due to various factors including complex environment, changes of illumination and scale. A moving vehicle tracking algorithm based on deep learning is proposed in this paper. First of all, traditional GMM algorithm is improved to reduce the error judgment probability of pixel state. Then, a sparse DAE neural network feature learning framework is proposed to ensure efficient extraction of vehicle features and reduce feature redundancy. Finally, the vehicle is tracked in its area. The experimental results show that the CM of proposed algorithm achieves 0.85 and has strong robustness.

A moving vehicle tracking algorithm based on deep learning

/pdf/background-subtraction-in-video-surveillance-1me5cup0j1.pdf

Background subtraction in Video Surveillance

Gaussian mixture models(GMM) is a widely used approach for background modeling. However, computational barriers have limited its usage in real-time video processing applications. In this paper, we discussed a new update algorithm to achieve the goal of fast detection. Dirichlet prior are introduced to avoid redundant Gaussian components, reducing the computation time of each pixel. Most of the existing GMM based techniques use background/foreground data proportion, which is highly sensitive to the environment, to detect object. To avoid its possible negative effects on segmentation, we use sigmoid function to approximate the probability of Gaussian component belongs to the background and set a threshold for it to segment. Experimental results show this method leads to a faster and a better segmentation than traditional GMM.

Fast moving object detection using improved Gaussian mixture models

There are many discontinuous skeleton points in human skeleton images, which make human skeleton behavior analysis be a difficult problem. Our paper presents a new breakpoint algorithm based on layer and partition of the neighborhood. We scan skeleton images line by line. The number of other skeleton points is calculated for each skeleton point in its 8-neighborhood. Then we judge whether this skeleton point is a breakpoint or not according to the number of the above-obtained and the distribution of other skeleton points in its 8-neighborhood. If it is, we will find available connection skeleton points which can connect the breakpoint. Finally, we find out the points that need to be updated to complete the breakpoint connection progress in accordance with linear equations established by the breakpoint and available connection points. Through of theory analysis and experiment verification, our method has good effect on connecting breakpoints in skeleton images and the shapes of skeleton images are undeformed. In addition to the human skeleton images, this method can also be used for other objects skeleton images on the breakpoints connection.

A New Method of Breakpoint Connection for Human Skeleton Image

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Fast moving object detection using improved Gaussian mixture models

A New Method of Breakpoint Connection for Human Skeleton Image