A Study on Motion Estimation

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.

International Journal of Intelligent Systems Technologies and Applications, 2010

Camera motion estimation plays an important role in digital video analysis algorithms such as video indexing and retrieval or automatic movie analysis. Several algorithms have been proposed to solve this problem in MPEG videos. This paper presents an optical flow-based approach for the camera motion estimation in all kinds of digital video formats, especially in movies. It compares the motion vector fields (MVFs) with six predefined templates to determine the type of motion. The MVFs are generated by using highaccuracy optical flow computation. The advantage of the method lies in its robustness to noisy environments such as false motion vectors and object motions. Comprehensive experiments with video clips extracted from well-known feature movies demonstrate the performance of the proposed approach.

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.

Motion estimation technique is the most vital component of any video coding standard. Therefore, development of an efficient method for fast motion estimation is the basic requirement of the video encoder design. Block based motion estimation algorithms are used to reduce the memory requirements of any video file and also decrease computational complexity. Motivated by the specific requirements of motion estimation, a variety of algorithms have been developed.

— In Digital video communication it is not practical, to store the full digital video without processing, because of the problems encountered in storage and transmission, so the processing technique called video compression is essential. In video compression, one of the computationally expensive and resource hungry key element is the Motion Estimation. The Motion estimation is a process which determines the motion between two or more frames of video. This paper addresses a comparison between block based motion estimation and pixel based motion estimation (ME) algorithms. We present the ME algorithms for both the methods, results of simulations and illustrate the analysis with PSNR values and computation time for different images. In image and video processing, the estimation of motion plays a vital role in video compression as well as multi-frame image enhancement. These applications share one common thread in all such applications, the demand is high for accurate estimates of motion requiring minimal computational cost.In the following paper we propose two methods for evaluation of the motion estimation algorithm: (1) motion estimation by pixel based approach and (2) Block based three step search technique.

Journal of Visual Communication and Image Representation, 2017

Detecting moving objects from video frame sequences has a lot of useful applications in computer vision. This proposed method of moving object detection first estimates the bi-directional optical flow fields between (i) the current frame and the previous frame and between (ii) the current frame and the next frame. The bi-directional optical flow field is then subjected to normalization and enhancement. Each normalized and enhanced optical flow field is then divided into non-overlapping blocks. The moving objects are finally detected in the form of binary blobs by examining the histogram based thresholded values of such optical flow field of each block as well as the optical flow field of the candidate flow value. Our technique has been conceptualized, implemented and tested on real video data sets with complex background environment. The experimental results and quantitative evaluation establish that our technique achieves effective and efficient results than other existing methods.

Motion estimation (ME) is a primary action for video compression. Actually, it leads to heavily to the compression efficiency by eliminating temporal redundancies. This approach is one among the critical part in a video encoder and can take itself greater than half of the coding complexity or computational coding time. Several fast ME algorithms were proposed as well as realized. In this paper, we offers a brief review on various motion estimation techniques mainly block matching motion estimation techniques. The paper additionally presents a very brief introduction to the whole flow of video motion vector calculation.

Electronics

Motion estimation has become one of the most important techniques used in realtime computer vision application. There are several algorithms to estimate object motions. One of the most widespread techniques consists of calculating the apparent velocity field observed between two successive images of the same scene, known as the optical flow. However, the high accuracy of dense optical flow estimation is costly in run time. In this context, we designed an accurate motion estimation system based on the calculation of the optical flow of a moving object using the Lucas–Kanade algorithm. Our approach was applied on a local treatment region implemented into Raspberry Pi 4, with several improvements. The efficiency of our accurate realtime implementation was demonstrated by the experimental results, showing better performance than with the conventional calculation.

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

International Journal of Innovative Technology and Exploring Engineering, 2019

Object tracking is a troublesome undertaking and significant extent in data processor perception and image handling community. Some of the applications are protection surveillance, traffic monitoring on roads, offense detection and medical imaging. In this paper a recent technique for tracking of moving object is intended. Optical flow information authorizes us to know the displacement and speed of objects personate in a scene. Apply optical flow to the image gives flow vectors of the points to distinguishing the moving aspects. Optical flow is accomplished by Lucas canade algorithm. This algorithm is superior to other algorithms. The outcomes reveals that the intend algorithm is efficient and accurate object tracking method. This paper depicts a smoothing algorithm to track the moving object of both single and multiple objects in real time. The main issue of high computational time is greatly reduced in this proposed work.

Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering, 2017

In video compression technique, motion estimation is one of the key components because of its high computation complexity involves in finding the motion vectors (MV) between the frames. The purpose of motion estimation is to reduce the storage space, bandwidth and transmission cost for transmission of video in many multimedia service applications by reducing the temporal redundancies while maintaining a good quality of the video. There are many motion estimation algorithms, but there is a trade-off between algorithms accuracy and speed. Among all of these, block-based motion estimation algorithms are most robust and versatile. In motion estimation, a variety of fast block based matching algorithms has been proposed to address the issues such as reducing the number of search/checkpoints, computational cost, and complexities etc. Due to its simplicity, the block-based technique is most popular. Motion estimation is only known for video coding process but for solving real life applications many researchers from the different domain are attracted towards block matching algorithms for motion vector estimation.This paper is a review of various block matching algorithms based on shapes and patterns as well as block matching criteria used for motion estimation.

Journal of Balkan Libraries Union, 2017

Humanity created different methods for sharing information. One of the first forms of sharing information and knowledge were images. In the beginning, the process of sharing was relying on static appearances. With the invention of moving pictures by Eadweard Muybridge in the first part of 1870s, this exchange and sharing gained a new quality. Now it was possible to show and preserve motion too. Since that time, technology has changed rapidly. The latest discoveries and improvements from the point of view of technology use computer and IT technologies extensively. Today it is possible for everybody to create and record movies by themselves using affordable and convenient technological devices. Also the process of sharing evolved rapidly and become cheaper and cheaper. We are now able to record some movies and share them through the Internet or other carriers in real time or near real time. However, this also creates serious problems due to the huge volume of data to be sent through the data lines. Therefore, research has concentrated on methods to decrease the data volume without losing the quality. One way to do that is to create effective CODECs. A major drawback of moving pictures is the motion itself. CODECs have to minimize the size of videos without paying the price of quality losses but have also to reduce the computational complexity. Both of these requirements can be achieved with a solid knowledge of motion estimation among others. This paper gives a general overview and survey of some existing and important approaches without the claim of having a complete overview of the field.

2010

For the recent years there was an increasing interest in different methods of motion analysis based on visual data acquisition. Vision systems, intended to obtain quantitative data regarding motion in real time are especially in demand. This paper talks about the vision systems that allow the receipt of information on relative object motion in real time. It is shown, that the algorithms solving a wide range of practical problems by definition of relative movement can be generated on the basis of the known algorithms of an optical flow calculation. One of the system’s goals is the creation of economically efficient intellectual sensor prototype in order to estimate relative objects motion based on optic flow. The results of the experiments with a prototype system model are shown. This research was supported in part by the grant of RFBR № 08-01-00908.

Lecture Notes in Computer Science, 2009

The estimation of camera motion is one of the most important aspects for video processing, analysis, indexing, and retrieval. Most of existing techniques to estimate camera motion are based on optical flow methods in the uncompressed domain. However, to decode and to analyze a video sequence is extremely time-consuming. Since video data are usually available in MPEG-compressed form, it is desirable to directly process video material without decoding. In this paper, we present a novel approach for estimating camera motion in MPEG video sequences. Our technique relies on linear combinations of optical flow models. The proposed method first creates prototypes of optical flow, and then performs a linear decomposition on the MPEG motion vectors, which is used to estimate the camera parameters. Experiments on synthesized and real-world video clips show that our technique is more effective than the state-of-the-art approaches for estimating camera motion in MPEG video sequences.

Artificial Intelligence

The objective of this work is to present an object tracking algorithm developed from the combination of random tree techniques and optical flow adapted in terms of Gaussian curvature. This allows you to define a minimum surface limited by the contour of a two-dimensional image, which must or should not contain a minimum amount of optical flow vector associated with the movement of an object. The random tree will have the purpose of verifying the existence of superfluous vectors of optical flow by discarding them, defining a minimum number of vectors that characterizes the movement of the object. The results obtained were compared with those of the Lucas-Kanade algorithms with and without Gaussian filter, Horn and Schunk and Farneback. The items evaluated were precision and processing time, which made it possible to validate the results, despite the distinct nature between the algorithms. They were like those obtained in Lucas and Kanade with or without Gaussian filter, the Horn and ...