Detection of moving objects based on enhancement of optical flow

Real-time detection of moving objects is vital for video surveillance. Background subtraction serves as a basic method typically used to segment the moving objects in image sequences taken from a camera. Some existing algorithms cannot fine-tune changing circumstances and they need manual calibration in relation to specification of parameters or some hypotheses for dynamic changing background. An adaptive motion segmentation and detection strategy is developed by using motion variation and chromatic characteristics, which eliminates undesired corruption of the background model and it doesn't look on the adaptation coefficient. In this particular proposed work, a novel real-time motion detection algorithm is proposed for dynamic changing background features. The algorithm integrates the temporal differencing along with optical flow method, double background filtering method and morphological processing techniques to achieve better detection performance. Temporal differencing is designed to detect initial motion areas for the optical-flow calculation to produce realtime and accurate object motion vectors detection. The double background filtering method is obtain and keep a reliable background image to handle variations on environmental changing conditions that is designed to get rid of the background interference and separate the moving objects from it. The morphological processing methods are adopted and mixed with the double background filtering to obtain improved results. The most attractive benefit for this algorithm is that the algorithm does not require to figure out the background model from hundreds of images and can handle quick image variations without prior understanding of the object size and shape.

International Journal of Computer Applications, 2015

The moving object detection and tracking in video sequence is significant research area in the field of computer vision and image processing. Object detection and tracking is key step for object recognition, navigation systems and surveillance systems. An effort has been made to develop an approach for moving object detection in video sequence, based on motion segmentation using optical flow and motion histogram. The algorithm has been described in detail and its competence was checked by performing simulation experiments. ROC comparison shows that this algorithm gives better result than traditional methods.

International Journal of Innovative Research in Computer and Communication Engineering, 2015

Real time moving object detection is sensing the physical moment in given particular area. For detecting the motion in real time there are multiple methods are available like Background Subtraction, Gaussian Mixture Model, Inter Frame Differencing, Kernel Density etc. Here for proposed work used Gaussian Mixture Model and Optical Flow technique for the moving object detection. Gaussian Mixture Model is better extract the foreground object but it more time consuming and Optical Flow is faster for moving object detection but it is not more accurate so by using these two approaches design new approach for detect the moving object in real time.

2012

This paper presents optical flow estimation technique to estimate the motion vectors in each frame of the video sequence. By thresholding and performing morphological closing on the motion vectors, we produces binary feature images. Using these binary features the cars are located. A bounding Box is drawn around the cars that pass beneath the white line. The algorithm used for this is lucas kanade. Use of the threshold to reduce the noise in small movements between frames is analyzed. Higher the threshold ,the less small movements impact the optical flow calculation. Experiments are done to find the value that best achieves our results.

2013

Differential methods of optical flow estimation are based on partial spatial and temporal derivatives of the image signal. In this paper, the comparison between background modeling technique and Lucas-Kanade optical flow has been done for object detection. Background subtraction methods need the background model from hundreds of images whereas the LucasKanade optical flow estimation method is a differential two frames algorithm, because it needs two frames in order to work. LucasKanade method is used which divides image into patches and computing a single optical flow on each of them. Keywords— Background Modeling, Motion Vector, Optical Flow, Object Detection

Journal of Next …, 2011

The detection of moving objects is a basic task for computer vision system. The performances of these systems are not sufficient for many applications. One of the main reasons is that the moving objet detection task has many difficulties in dealing with various constraints like the variations of the environment. A great number of methods were already proposed. We classify contributions reported in the literature in four approaches with a categorization based on inter-frame processing they adopt methods based on Inter-Frame Difference (IFD), those based on Background Modeling (BM), methods based on the Optical Flow (OF), and hybrid methods. In this paper, we present our proposed methods to detect moving objects. The first is a hybrid method that combines the inter-images difference based on entropy image and optical flow computed by a local method with a hierarchical coarse-to-fine optical flow estimation. The second is an adaptive background modeling based on dynamic matrix and spatio-temporal analyses of scenes. A comparative study by quantitative evaluations shows that the proposed BM method can detects foreground robustly and accurately from videos recorded by a static camera and which include several constraints such as sudden and gradual illumination changes, shaking camera, background component changes, ghost, and foreground speed.

2009

Abstract—Motion detection is very important in image processing. One way of detecting motion is using optical flow. Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. The method used for finding the optical flow in this project is assuming that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image.

SN Computer Science, 2020

Moving object detection and tracking from video sequences are a relevant research field since it can be used in many applications. While detection allows to return object shapes discovered in the image, tracking aims to individually identify and estimate individual trajectories of detected objects over time. Hence, detection can have a crucial impact on the overall tracking process. This paper focuses on detection. Currently, one of the leading detection algorithms includes frame difference method (FD), background subtraction method (BS), and optical flow method. Here, we present a detection algorithm based on the first two approaches since it is very adequate for fast real-time treatments, whereas optical flow has higher computation cost due to a dense estimation. A combination of FD and BS with Laplace filters and edge detectors is a way to achieve sparse detection fast. Thus, a main proposed contribution is the achievement of a systematic detection algorithm for moving target detection with a more elaborated combination of basic procedures used in real-time surveillance. Experimental results show that the proposed method has higher detection accuracy and better noise suppression than the current methods for standard benchmark datasets.

2015

The analysis of human body motion is an important method in which computer vision combines with bio- mechanics. This method is widely used in motion detection, motion analysis, intelligent control and many other fields. In the analysis of human body motion; the moving human body detection is important part. The moving human body is detected from the background image in video sequences. Here the new method for the moving object detection based on background subtraction is defined by establishing a reliable background updating model which uses a dynamic optimization threshold method to obtain a more complete moving object. After getting moving object to remove the noise morphological filtering is done. The noise is in form of disturbances which present in the background.

— This article deals with methods for automatic detection of moving objects in non-standard situations in secure areas, such as nuclear power plants, storage of hazardous materials, etc. Speed and direction, respectively, slow motion to a stop is detected for the monitored object. Specifically: the automatic detection of movement in the opposite direction than normal, passage or move too fast, and stopping or blocking passages. The emphasis is put on real-time processing and on design effective and efficient methods. A new approach is a combination and modification of optical flow techniques and Mixture Of Gaussians method (MOG).

International Journal of Computer Applications, 2014

Systems and Computers in Japan, 2002

A scheme for detecting a moving object in a three-dimensional environment from observed dynamic images by optical flow, based on the state of the motion of the observing system, is proposed in this paper. The usual optical flow constraint equations defined in an image coordinate system do not sufficiently satisfy the assumptions made in deriving them when the observing system is in motion. In this paper, optical flow constraint equations considering the motion of the observing system are first derived. In order to do this, a mapping converting the motion of a stationary environment image to linear trajectory signals is derived. The uniform velocity property of motion and the isotropic property of motion within a proximal area, which are basic assumptions of the block gradient method, can be satisfied by these. Next, a method of expressing the optical flow constraint equations after mapping by the gradient in the time dimension before mapping is presented. Finally, the residuals of the optical flow constraint equations are proposed as the evaluation quantity for the extraction of a moving object and their efficacy is shown. © 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 33(6): 83–92, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.1135

IET Image Processing, 2020

Segmentation of moving object in video with moving background is a challenging problem and it becomes more difficult with varying illumination. The authors propose a dense optical flow-based background subtraction technique for object segmentation. The proposed technique is fast and reliable for segmentation of moving objects in realistic unconstrained videos. In the proposed work, they stabilise the camera motion by computing homography matrix, then they perform statistical background modelling using single Gaussian background modelling approach. Moving pixels are identified using dense optical flow in the background modelled scenario. The dense optical flow provides motion information of each pixel between consecutive frames, therefore for moving pixel identification they compute motion flow vector of each pixel between consecutive frames. To distinguish between foreground and background pixels, they labelled each pixel and thresholding the magnitude of motion flow vector identifies the moving pixels. The effectiveness of the proposed algorithm has been evaluated both qualitatively and quantitatively. The proposed algorithm has been evaluated on several realistic videos of different complex conditions. To assess the performance of the proposed work, the authors compared their algorithm with other state-of-art methods and found that the proposed method outperforms the other methods.

International Journal of Computer Applications, 2013

During the last few years different video-surveillance systems have been developed based on video processing and using different techniques. This surveillance system generally seeks to track people (and/or vehicles) moving through a scene, to classify the behaviors of each track, and to identify whether these behaviors can be considered normal or abnormal. All Automated surveillance systems require some mechanism to detect interested objects in the field of view of the sensor. Once objects are detected, the further processing for tracking. In my paper a method is described for tracking moving objects from a sequence of video frame. This method is implemented by using optical flow (Horn-Schunck) and Region filtering in matlab simulink. The objective of this paper is to identify and track a moving object within a video sequence for both Abrupt change video as well as Gradual change video in video surveillance.

Proceedings of Advanced Concepts for Intelligent …

Automatic moving object detection/extraction has been explored extensively by the computer vision community. Unfortunately majority of the work has been limited to stationary cameras, in which background subtraction is utilized as the major methodology. In this paper, we will present a technique to tackle the problem in the case of moving camera which is the most often encountered situation in real life for target tracking, surveillance, etc. Instead of focusing on two adjacent time frames, our object detection rests on three consecutive video frames, a backward frame, the frame of interest and a forward frame. Firstly, optical flow based simultaneous iterative camera motion compensation and background estimation is carried out on backward and forward frames. Differences between camera motion compensated backward and forward frames with the frame of interest are then tested against the estimated background models for intensity change detection. Next, these change detection results are combined together for acquiring approximate shape of the moving object. Experimental results for a video sequence with moving camera are presented.