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The Program for Optical Flow Estimation Based on Deep Learning Approaches

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Размещено: 13.12.2020
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Введение

1. INTRODUCTION Images are great in all; videos are even more resourceful because there is more information in a video than in an image. Image exposes merely the spatial positioning of the pixels, thus the relative position of each pixel but in a video, there exists a set of individual film frames or video frames, which are typically many still images, that sequentially compose the complete moving pictures to make up the video over time. So, a video contains the same spatial information and an additional temporal component. Meaning that not only the location of a pixel exits but also when this pixel value assumes any particular location. This additional information gives knowledge about the time and duration of the pixel. In other words, information in a video is encoded not only spatially, but also sequentially with respect to time. This amount of information opens a lot of doors on what could be investigated and made processing of videos more interesting. Optical flow is one of these doors. It is a technique use to track the motion of objects in videos. This technique has a number of different applications including video compression, video stabilization, video description (a more recent application area), object detection and tracking, velocity estimation, and to mention but a few. Optical flow estimation is a per-pixel prediction which means it estimates the displacement of the pixel brightness as they travel across the video frames [9]. Optical flow is a per-pixel prediction which means to estimate how the pixel brightness moves across the screen over time [10],[11]. With the recent advancements and benefits of AI, Deep Neural Networks are becoming a popular approach to solving problems. They enable computers learn features from images and videos to predict certain behaviour. Imagine if athletes run with a camera on their chest, could the speed of the athlete be determined in real time? Given a video from the dashboard camera of a moving car, can the speed of 1 the car be determined? If that of a car would be possible, why can it not work for athletics as well. The relevance of this idea is not only in the above-mentioned applicable areas but also could be used in understanding people flow. In the paper by Hara et al, a method to estimate the flow of pedestrians was proposed and accomplished with the help of a Convolution Neural Network (CNN) [12]. This was estimated from dashboard camera video. Determining the speed of a car can be very challenging but with labelled data and a CNN architecture and with help of optical flow, it might be feasible to estimate the speed. Let us explore optical flow a little further and proceed with a little further insight on vehicle speed estimation. Determining the speed of a car can be very challenging but with labelled data and a CNN architecture and with help of optical flow, we might be able to estimate the speed.
Содержание

Table of Contents ABSTRACT............................................................................................................... v Keywords ................................................................................................................ v Terms and Abbreviations ...................................................................................... vi 1. INTRODUCTION ............................................................................................... 1 1.1. Background.................................................................................................... 2 1.1. Optical Flow Estimation ................................................................................ 4 1.2. Vehicle Speed Estimation ............................................................................. 8 1.3. Deep Learning ............................................................................................... 9 1.4. Problem Statement ...................................................................................... 15 1.5. Relevance and Motivation ........................................................................... 15 1.6. Research Question ....................................................................................... 15 1.7. Goal ............................................................................................................. 15 1.8. Objective...................................................................................................... 15 1.9. Conclusion ................................................................................................... 16 2. LITERATURE REVIEW .................................................................................. 17 2.1. Classical Methods for Determination .......................................................... 17 2.1.1. Lucas-kanade......................................................................................... 17 2.1.2. Horn and Schunck ................................................................................. 18 2.2. State of the Arts Methods for Determination .............................................. 18 2.2.1. FlowNet ................................................................................................. 18 2.2.2. FlowNet2.0 ............................................................................................ 19 2.2.3. SpyNet ..................................
Список литературы

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Отрывок из работы

ABSTRACT The recent advancement in artificial intelligence (AI) and the rapid increase in the computation ability of modern computers has boosted capacities in solving problems. One of the goals of computer science is to enable computers to see like humans. This is somewhat achieved through image and video processing and analysis under the computer vision branch of computer science [1]. Just as the human visual system has a stimulus for the perception of the shape, distance and movement of objects in the real world and control of locomotion, it is also vital for computers to be able to do the same [5]. Videos come with a great amount of information, analyzing them enable us to handle problems such as motion detection and tracking as well as speed estimation. Speed estimation of moving objects from video cameras is one of the most important topics in the field of computer vision. It is one of the key pieces to look at in transport systems, robotics, military and naval operations, sports and among others. In this study, we explore modern optical flow methods which apply deep learning approaches and prioritizes motion as a key characteristic of classification [2]–[4] and use convolutional neural networks (CNN) to predict, with better accuracy, the speed of a car from a car dashboard camera footage.
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