Unsupervised Moving Object Detection through Background Models for PTZ Camera

Moving Object Detection systems have long been in use to look at security sensitive areas. The manufacture of video investigating systems sensible desires fast, reliable and durable algorithms for moving object detection classification pursuit and activity analysis. Moving object detection is that the essential step for extra analysis of video. It handles segmentation of moving objects from stationary background objects. Object classification step categorizes detected objects into preened classes like human, vehicle, animal, clutter, etc. it's a necessity to inform apart objects from each other therefore on trace and analyse their actions reliably. In previous system we have got performed background subtraction by victimization clever Edge Detection. In clever Edge Detection technique we tend to tend to square measure taking 2 photos for comparison those square measure background image and foreground image. Previous strategies for object detection square measure methodology along side object detectors image segmentation and Background subtraction. Our methodology aims to section objects supported motion info and it contains a part of background modelling. In the previous strategies we tend to tend to square measure conducting background subtraction only for photos. For this we tend to tend to projected an image component wise background modelling and subtraction technique mistreatment multiple choices. Hence, throughout this color, gradient and Hear-Like choices square measure integrated to handle the variation element. Thus, motion segmentation and background subtraction square measure the foremost connected topics to the current paper.

Moving Object Detection (MOD) from satellite videos plays one of the most fundamental roles in satellite video surveillance. Training a supervised moving object detector typically requires boundary box annotations for object instances, however, this annotation process is time-consuming for satellite videos. In this paper, we propose a weakly supervised method for sidestepping this process, where the supervised information for detecting moving objects on a frame is instead provided by the unsupervised method based on motion knowledge across a video. We adopt the Extended Low-rank and Structured Sparse Decomposition (E-LSD) approach for generating pixel-wise pseudo labels for moving objects. Then the extracted pseudo labels are used for training a Deep Convolutional Neural Network (DCNN) following the Encoder-Decoder architecture with lateral connections for segmenting moving objects from a new frame. We demonstrate the effectiveness of the proposed method on a satellite video dataset, and, compared with five state-of-the-art MOD methods tested, it achieves both improved detection accuracy and promising frame rate.

Moving object detection in video satellite image is studied. A detection algorithm based on deep learning is proposed. The small scale characteristics of remote sensing video objects are analyzed. Firstly, background subtraction algorithm of adaptive Gauss mixture model is used to generate region proposals. Then the objects in region proposals are classified via the deep convolutional neural network. Thus moving objects of interest are detected combined with prior information of sub-satellite point. The deep convolution neural network employs a 21-layer residual convolutional neural network, and trains the network parameters by transfer learning. Experimental results about video from Tiantuo-2 satellite demonstrate the effectiveness of the algorithm.

Automated video surveillance using video analysis and understanding technology has become an important research topic in the area of computer vision. Most cameras used in surveillance are fixed, allowing to only look at one specific view of the surveilled area. Recently, the progress in sensor technologies is leading to a growing dissemination of Pan-Tilt-Zoom (PTZ) cameras, that can dynamically modify their field of view. Since PTZ cameras are mainly used for object detection and tracking, it is important to extract moving object regions from images taken with this type of camera. However, this is a challenging task because of the dynamic background caused by camera motion.

Moving objects have been detected using various object detection techniques. Two categories for moving object detection techniques are frame differencing based and background subtraction based. These techniques are limited by camera scene complexity, light conditions, video type etc. Frame differencing based techniques process videos faster compared to background subtraction based techniques. Frame differencing based techniques detects only the boundary of the moving object and may fail for slow moving objects. These techniques for moving object detection can be improved by using sound data as most video recording cameras are equipped with a microphone. Sounds from human footsteps can be recorded with video and used with frame differencing techniques to improve moving object detection results. Camera microphones also record background noise with other background sound. This noisy data has been filtered out using the Fourier transform. When peak locations for each footstep sound are determined, and a Full Width at Half Maxima is computed for each peak, the number of frames within this width are counted, these frames are verify the presence of a moving object.

Moving object detection is an important and basic step in any video surveillance application. Detected object has used an input to higher level tasks such as tracking, event detection, and behavioral analysis. There are several challenges in moving object detection such as illumination changes, occlusion, shadow, bootstrap, dynamic background etc. Detection of moving object having similar color as background structure is called as camouflage, which is another big challenge. The method of detecting camouflage moving object is known as Decamouflaging. In recent years, the researchers are attracted towards the camouflage moving object detection due to its application in the military. In this review paper, different techniques using various features for camouflaged moving obj ect detection were studied.

Moving object detection in urban scenes is important for the guidance of autonomous vehicles, robot navigation, and monitoring. In this paper moving objects are automatically detected using three sequential frames and tracked over a longer period. To this extend we modify the plane+parallax, fundamental matrix, and trifocal tensor algorithms to operate on three sequential frames automatically, and test their ability to detect moving objects in challenging urban scenes. Frame-to-frame correspondences are established with the use of SIFT keys. The keys that are consistently matched over three frames are used by the algorithms to distinguish between static objects and moving objects. The tracking of keys for the detected moving objects increases their reliability over time, which is quantified by our results. To evaluate the three different algorithms, we manually segment the moving objects in real world data and report the fraction of true positives versus false positives. Results show that the plane+parallax method performs very well on our datasets and we prove that our modification to this method outperforms the original method. The proposed combination of the advanced plane+parallax method with the trifocal tensor method improves on the moving object detection and their tracking for one of the four video sequences.

Moving object detection is one of the most important tasks in intelligent visual surveillance systems. Gaussian Mixture Model (GMM) has been most widely used for moving object detection, because of its robustness to variable scenes. However, to the best of our knowledge, existing GMM based methods can not detect moving objects which gradually stop and keep still state for a while. In this paper, we present an Enhanced Gaussian MixtureModel, called EGMM, to handle this problem. We integrate an Initial Gaussian Background Model (IGBM) and an extended Kalman filter based tracker with GMM, to enhance its performance. Experimental results show that our EGMM based method has a lower miss rate at the same false positives per image comparing to GMM based method for moving pedestrian detection, and it also has a higher detection rate for abandoned object detection comparing to GMM based method.

Moving object detection under varying illumination is a prominent area of research in the field of computer vision. The detected object results from moving object framework, suffers from factors like noise, change in object scene, shadow, changes in illumination etc. So there is a growing interest to develop a robust moving object detection framework under varying illumination scenario. A significant amount of methods have been proposed over recent years on moving object detection under varying illumination condition. In this paper some of the recent effective methods based on accuracy have been reviewed. In this paper, methods reviewed include illumination sensitive feature and illumination invariant feature approaches for moving object detection under illumination varying background conditions. Methods reviewed in this paper include qualitative and quantitative comparison to guide the reader a clear insight to the computer vision community, those who are especially working in moving object detection under different illumination conditions.

Moving object detection is an important research hotspot in the field of image processing, which is the basis of object classification and object tracking. On the one hand, moving object detection should adapt to the changes of environment and itself, on the other hand, it should take into account the accuracy and real-time of detection. ViBe algorithm is a simple and fast algorithm in moving object detection methods. It has no specific requirements for video format, color space, and background relationship. It only needs a small number of sample values to cover the background samples, and has both accuracy and computational efficiency. How to choose parameters is the key problem to determine the performance of ViBe algorithm. This paper mainly studies the ViBe detection method for moving objects, and optimizes the main parameters of the ViBe algorithm through simulation. Firstly, the sample radius is selected according to the target resolution; Secondly, the updating factor of time parameters and the number of samples in the pixel model are optimized; Finally, the minimum matching number is selected to constrain the number of matching samples to classify new pixels. The experimental results show that the method has good performance in moving object detection and can retain the integrity of moving objects.

Moving object detection is an important component of a traffic surveillance system. Usual background subtraction approaches often poorly perform on a long outdoor traffic video due to vehicles waiting at an intersection and gradual changes of illumination and background shadow position. We present a fast and robust background subtraction algorithm based on unified spatio-temporal background and foreground modeling. The correlation between neighboring pixels provides high levels of detection accuracy in the dynamic background scene. Our Bayesian fusion method, which establishes the traffic scene model, combines both background and foreground models and considers prior probabilities to adapt changes of background in each frame. We explicitly model both temporal and spatial information based on the kernel density estimation (KDE) formulation for background modeling. Then, we use a Gaussian formulation to describe the spatial correlation of moving objects for foreground modeling. In the updating step, a fusion background frame is generated, and reasonable updating rates are also proposed for the traffic scene. The experimental results show that the proposed method outperforms the previous work with less computation and is better suited for the traffic scenes.

Moving object detection and feature extraction algorithm in video sequences are discussed in this paper. There are several problems in moving object detection and extraction from outdoor video surveillance, that is, moving object detection algorithm is easily interfered by background of video monitor, the feature of moving object is difficult to extract from video source, and the vibration of picture frame in outdoor video surveillance causing by wind factors effects the incorrect extraction of the moving object. The vibration causing by wind factors was corrected and an enhancement inter-frame difference algorithm based on difference histogram threshold selection is presented in this paper. The experiment results prove that this method can detect and extract the moving object accurately and efficiently, and it can meet the needs of real-time detection.

Moving object detection with a mobile image sensor is an important task when considering mobile robots for use in human environments. In this paper, we propose a novel method/or effectively solving the problem of detecting moving objects for mobile robots by using the stereo omni-directional system (SOS) which has a complete spherical FOV. We first predict the depth image for the present time from the self-motion of the SOS and the depth image obtained at the previous time, and then detect the moving objects by comparing the predicted depth image with the actual one obtained at the present time. Experiments in the real world show the effectiveness of the proposed method.

Moving object detection is a fundamental step in various computer vision applications. Robust Principal Component Analysis (RPCA) based methods have often been employed for this task. However, the performance of these methods deteriorates in the presence of dynamic background scenes, camera jitter, camouflaged moving objects, and/or variations in illumination. It is because of an underlying assumption that the elements in the sparse component are mutually independent, and thus the spatiotemporal structure of the moving objects is lost. To address this issue, we propose a spatiotemporal structured sparse RPCA algorithm for moving objects detection, where we impose spatial and temporal regularization on the sparse component in the form of graph Laplacians. Each Laplacian corresponds to a multi-feature graph constructed over superpixels in the input matrix. We enforce the sparse component to act as eigenvectors of the spatial and temporal graph Laplacians while minimizing the RPCA objective function. These constraints incorporate a spatiotemporal subspace structure within the sparse component. Thus, we obtain a novel objective function for separating moving objects in the presence of complex backgrounds. The proposed objective function is solved using a linearized alternating direction method of multipliers based batch optimization. Moreover, we also propose an online optimization algorithm for real-time applications. We evaluated both the batch and online solutions using six publicly available datasets that included most of the aforementioned challenges. Our experiments demonstrated the superior performance of the proposed algorithms compared with the current state-of-the-art methods.

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.