Moving Object Detection Using Integrated Spatial and Motion-Based Method

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.

To detect moving objects is a difficult task for various image processing and computer vision applications viz., motion segmentation, object classification and identification, behavior understanding, event detection, object tracking and object locating. The process becomes even more difficult with moving camera as compared to static camera as both camera and object motions are combined in the detection process. Moreover, almost all real time video sequences incorporate pan/tilt/zoom camera which makes it essential to detect objects in moving cameras. This paper presents a survey of various moving object detection techniques in moving cameras and gives insight picture of the methods like background subtraction, optical flow, feature based object detection and blob analysis. It also mentions pros and cons of every technique used so far individually. The use of these approaches and progress made over years has been tracked and elaborated.

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 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 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 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 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 based on monitoring video system is often a challenging problem. Specially to moni- tor traffic at both day and night, in different weather and illumination conditions and with changeable background. Kernel Density Estimation (KDE) model is an effective approach to judge background and foreground, however, typical KDE uses fixed parameters, such as bandwidths, threshold, etc. This paper proposes a detection algorithm based on an Im- proved Kernel Density Estimation (IKDE) model. The proper bandwidths, adaptive background sample learning array, and adaptive threshold, and an improved sample updating method for sample learning array are discussed as the funda- mentals of the IKDE model. Furthermore, an algorithm for restraining light field disturbance at night in video scene is proposed. Video image series are evaluated through the algorithm, and moving object detection is conducted in three dif- ferent scenes. Results show that the algorithm can help to achieve a promising high accuracy and robustness for detecting moving objects.

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 (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 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.

We discuss a problem of automatic defect detection in welded joints of stainless steel pipes in the production process. Possible defects that occur during tungsten inert gas welding are shown. The substantiation of the choice of the method for solving the problem based on modeling and background subtraction is given. An algorithm for defect detection in welded joints on frames of video sequences is proposed, taking into account the features of a specific area. The background models are built using the methods of averaging and a mixture of Gaussians. Experimental studies of the algorithm are carried out using examples of processing frames of video sequences received from a static camera. The obtained results confirm that the background modeling method based on frame averaging is suitable for the automatic detection of welding defects since the defects are different and have characteristic features. The proposed algorithm makes it possible to detect and highlight the defective area in a welded joint on frames of video sequences. The experimental results show that the algorithm satisfies the requirements for continuous rapid detection of surface defects.

Effective vehicle detection technique for traffic surveillance systems

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.