Detection of moving objects from a moving platform in urban 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 in a video plays an important role in many vision applications. Recently, moving object detection using appearance modeling based on a convolutional neural network has been actively developed. However, the CNN- based methods usually require the user’s supervision of the first frame so that it becomes highly dependent on the training dataset. In contrast, the method of finding a foreground, which models a background occupying a large proportion in an image, can detect a moving object efficiently in an unsupervised manner. However, existing methods based on background modeling in a pan-tilt-zoom (PTZ) camera suffer many false positives or loss of moving objects due to the estimation error of camera motion. To overcome the aforementioned limitations, we propose a moving object detection method for a PTZ camera through two background models. In an unsupervised way, our method builds the two background models that have different roles: 1) a coarse background model for detecting large changes, and 2) a fine background model for detecting small changes. In more detail, the coarse background model builds a block-based Gaussian model, and the fine model builds a sample consensus model. Both models are adaptively updated according to the estimated camera motion in the video recorded by a PTZ camera. Then, each foreground result from two background models is incorporated to fill the moving object region. Through experiments, the proposed method achieves better performance than the state-of-the-art methods and operates in real-time without parallel processing. In addition, we showed the effectiveness of the proposed model through improved results of moving object detection through combination with the latest supervised method.

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 objects' detection in dynamic scenes is a very important task in video processing. In the applications of image processing (for example, video-surveillance), more attention is paid to whether there is an interested object in the scene rather than where the object is located. As a matter of fact, similar to static backgrounds, the statistical histograms of the most dynamic backgrounds have favorable stability, and these histograms would change clearly only when big and contrasting objects enter or move out of the scenes. This is a very important and interesting property for dynamic background. We propose a fast and simple algorithm, combining histograms in multiple color spaces and the superposition principle of statistical histogram, called Multiple Color Space Histogram Models (MCSHM). MCSHM first calculates statistical histograms of many color components in multiple color space and then use the changes of statistical histograms to determine whether there is an object rather than the changes in pixels or pixel-level regions. Thus, the computational complexity of MCSHM is kept at a very low level. The basic steps are as follows: firstly, convert each frame from RGB space to other color spaces and calculate the histograms of selected color components, then we can obtain the background histogram model; secondly, detect the objects using statistical histogram superposition principle; finally, update MCSHM by the result of detection. The experimental results demonstrate that our method can quickly and accurately detect moving objects in dynamic scenes.

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.

In recent years, the numbers of Visual Surveillance systems have greatly increased, and these systems have developed into intellectual systems that automatically detect, track, and recognize objects in video. Automatic moving object detection and tracking is a very challenging task in video surveillance applications. In this regard, many methods have been proposed for Moving Object Detection and Tracking based on edge, color, texture information. Due to unpredictable characteristics of objects in foggy videos, the task of object detection remains a challenging problem. In this paper, we propose a novel scheme for moving object detection based on Log Gabor filter (LGF) and Dominant Eigen Map (DEM) approaches. Location of the moving object is obtained by performing connected component analysis. In turn, a Moving Object is Tracked based on the centroid manipulation. Number of experiments is performed using indoor and outdoor video sequences. The proposed method is tested on standard PETS datasets and many real time video sequences. Results obtained are satisfactory and are compared with existing well known traditional methods.

Moving object detection is vitally used in video surveillance applications. Traditional Gaussian mixture model (GMM) based background subtraction (BGS) methods are usually performs well when background is stationary. However, they require parameter tuning to deal with dynamic backgrounds, whose background pixel values change over time. Particularly, the threshold which determines the pixels associated with moving objects from the resultant of BGS. To tackle this problem there is no ultimate solution. Considering that, this paper intents to present a novel idea to update the threshold of GMM based BGS with respect to color distortion, similarity and illumination measures in pixel level. Extensive experiments were carried out to demonstrate the effectiveness of the proposed method in comparison to some of the long-familiar GMM based BGS methods in literature. However, note that this paper is not attempted to provide a real-time technique, but rather to investigate the potential utilization of the aforementioned measures to set a threshold automatically to detect moving objects in video sequences.

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 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 one of the key tasks in image processing due to its important role in many applications nowadays. In spite of the fact that many relevant active researches exist, these methods still have drawbacks. This paper proposes an integrated method to detect moving object in video sequence frames. To make the detection more robust, we adopt two well-known methods namely, optical flow and active contour, and added a new adaptive spatial thresholding. The optical flow is first applied on two sequence frames to differentiate the shift of flow vectors of these two. Then, the lost areas are retrieved by our adaptive spatial thresholding. Depending upon the intensity and spatial correlation of individual pixel, they can improve the accuracy and robustness of segmentation process. In the final step, active contour converges to unite moving areas into one. The experimental results show that our method outperforms the previous motion-based methods, especially in term of processing time.

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.

Moving object detection is critical for automated video analysis in many vision-related tasks, such as surveillance tracking, video compression coding, etc. Robust Principal Component Analysis (RPCA), as one of the most popular moving object modelling methods, aims to separate the temporally-varying (i.e., moving) foreground objects from the static background in video, assuming the background frames to be low-rank while the foreground to be spatially sparse. Classic RPCA imposes sparsity of the foreground component using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> -norm, and minimizes the modeling error via <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _2$</tex-math></inline-formula> -norm. We show that such assumptions can be too restrictive in practice, which limits the effectiveness of the classic RPCA, especially when processing videos with dynamic background, camera jitter, camouflaged moving object, etc. In this paper, we propose a novel RPCA-based model, called Hyper RPCA, to detect moving objects on the fly. Different from classic RPCA, the proposed Hyper RPCA jointly applies the maximum correntropy criterion (MCC) for the modeling error, and Laplacian scale mixture (LSM) model for foreground objects. Extensive experiments have been conducted, and the results demonstrate that the proposed Hyper RPCA has competitive performance for foreground detection to the state-of-the-art algorithms on several well-known benchmark datasets.

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 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.