Global ETD Search

1	Incorporating Omni-Directional Image and the Optical Flow Technique into Movement Estimation Chou, Chia-Chih 30 July 2007 (has links) From the viewpoint of applications, conventional cameras are usually limited in their fields of view. The omni-directional camera has a full range in all directions, which gains the complete field of view. In the past, a moving object can be detected, only when the camera is static or moving with a known speed. If those methods are employed to mobile robots or vehicles, it will be difficult to determine the motion of moving objects observed by the camera. In this paper, we assume the omni-directional camera is mounted on a moving platform, which travels with a planar motion. The region of floor in the omni-directional image and the brightness constraint equation are applied to estimate the ego-motion. The depth information is acquired from the floor image to solve the problem that cannot be obtained by single camera systems. Using the estimated ego-motion, the optical flow caused by the floor motion can be computed. Therefore, comparing its direction with the direction of the optical flow on the image leads to detection of a moving object. Due to the depth information, even if the camera is in the condition that combining translational and rotational motions, a moving object can still be accurately identified. Omni-directional image Moving object Optical flow Ego-motion
2	On Vergence Calibration of a Stereo Camera System Jansson, Sebastian January 2012 (has links) Modern cars can be bought with camera systems that watch the road ahead. They can be used for many purposes, one use is to alert the driver when other cars are in the path of collision. If the warning system is to be reliable, the input data must be correct. One input can be the depth image from a stereo camera system; one reason for the depth image to be wrong is if the vergence angle between the cameras are erroneously calibrated. Even if the calibration is accurate from production there's a risk that the vergence changes due to temperature variations when the car is started. This thesis proposes one solution for short-time live calibration of a stereo camera system; where the speedometer data available on the CAN-bus is used as reference. The motion of the car is estimated using visual odometry, which will be affected by any errors in the calibration. The vergence angle is then altered virtually until the estimated speed is equal to the reference speed. The method is analyzed for noise and tested on real data. It is shown that detection of calibration errors down to 0.01 degrees is possible under certain circumstances using the proposed method. Ego-motion Self-calibration Stereo Calibration Stereo Camera Vergence Calibration Visual Odometry
3	A Study in 3D Structure Detection Implementing Forward Camera Motion , D.M.BAPPY, RAHMAN, MD HAMIDUR January 2012 (has links) In this thesis we have studied detection of 3D structures having a forward camera movement which has strong influence of translation along the optical axis of the camera. During the forward movement the camera might undergoes rotation and translation .We have used “Plane plus Parallax” algorithm to cancel out this unwanted rotation. The input to the algorithm is a sequence of frames aligned with respect to a certain planar surface. The algorithm gives three types of outputs. (i) Dense correspondence across all frames. (ii) Dense 3D structure relative to the planar surface. (ii) Focus of Expansion (FOE) in all frames with respect to reference frame. Camera calibration is not needed for this algorithm. We have applied this algorithm to real world images and synthetic images. In both cases the 3D structure information could be obtained clearly even for objects far from the reference plane. Our result shows the potential of the method in 3D reconstruction implementing ego-motion of a single camera. / dm_aiub@yahoo.com 008801681006314 3D RECONSTRUCTION PLANAR PARALLAX EGO-MOTION Computer Sciences Datavetenskap (datalogi) Signal Processing Signalbehandling Telecommunications Telekommunikation
4	Unsupervised Learning for Structure from Motion Örjehag, Erik January 2021 (has links) Perception of depth, ego-motion and robust keypoints is critical for SLAM andstructure from motion applications. Neural networks have achieved great perfor-mance in perception tasks in recent years. But collecting labeled data for super-vised training is labor intensive and costly. This thesis explores recent methodsin unsupervised training of neural networks that can predict depth, ego-motion,keypoints and do geometric consensus maximization. The benefit of unsuper-vised training is that the networks can learn from raw data collected from thecamera sensor, instead of labeled data. The thesis focuses on training on imagesfrom a monocular camera, where no stereo or LIDAR data is available. The exper-iments compare different techniques for depth and ego-motion prediction fromprevious research, and shows how the techniques can be combined successfully.A keypoint prediction network is evaluated and its performance is comparedwith the ORB detector provided by OpenCV. A geometric consensus network isalso implemented and its performance is compared with the RANSAC algorithmin OpenCV. The consensus maximization network is trained on the output of thekeypoint prediction network. For future work it is suggested that all networkscould be combined and trained jointly to reach a better overall performance. Theresults show (1) which techniques in unsupervised depth prediction are most ef-fective, (2) that the keypoint predicting network outperformed the ORB detector,and (3) that the consensus maximization network was able to classify outlierswith comparable performance to the RANSAC algorithm of OpenCV. sfm structure from motion depth ego-motion unsupervised learning consensus maximization Computer Sciences Datavetenskap (datalogi)
5	Semi-supervised learning for joint visual odometry and depth estimation Papadopoulos, Kyriakos January 2024 (has links) Autonomous driving has seen huge interest and improvements in the last few years. Two important functions of autonomous driving is the depth and visual odometry estimation.Depth estimation refers to determining the distance from the camera to each point in the scene captured by the camera, while the visual odometry refers to estimation of ego motion using images recorded by the camera. The algorithm presented by Zhou et al. [1] is a completely unsupervised algorithm for depth and ego motion estimation. This thesis sets out to minimize ambiguity and enhance performance of the algorithm [1]. The purpose of the mentioned algorithm is to estimate the depth map given an image, from a camera attached to the agent, and the ego motion of the agent, in the case of the thesis, the agent is a vehicle. The algorithm lacks the ability to make predictions in the true scale in both depth and ego motion, said differently, it suffers from ambiguity. Two extensions of the method were developed by changing the loss function of the algorithm and supervising ego motion. Both methods show a remarkable improvement in their performance and reduced ambiguity, utilizing only the ego motion ground data which is significantly easier to access than depth ground truth data Depth Estimation Visual Odometry Ego Motion 3D Reconstruction Semi-Supervised Autonomous Driving Computer Sciences Datavetenskap (datalogi)
6	ROBUST BACKGROUND SUBTRACTION FOR MOVING CAMERAS AND THEIR APPLICATIONS IN EGO-VISION SYSTEMS Sajid, Hasan 01 January 2016 (has links) Background subtraction is the algorithmic process that segments out the region of interest often known as foreground from the background. Extensive literature and numerous algorithms exist in this domain, but most research have focused on videos captured by static cameras. The proliferation of portable platforms equipped with cameras has resulted in a large amount of video data being generated from moving cameras. This motivates the need for foundational algorithms for foreground/background segmentation in videos from moving cameras. In this dissertation, I propose three new types of background subtraction algorithms for moving cameras based on appearance, motion, and a combination of them. Comprehensive evaluation of the proposed approaches on publicly available test sequences show superiority of our system over state-of-the-art algorithms. The first method is an appearance-based global modeling of foreground and background. Features are extracted by sliding a fixed size window over the entire image without any spatial constraint to accommodate arbitrary camera movements. Supervised learning method is then used to build foreground and background models. This method is suitable for limited scene scenarios such as Pan-Tilt-Zoom surveillance cameras. The second method relies on motion. It comprises of an innovative background motion approximation mechanism followed by spatial regulation through a Mega-Pixel denoising process. This work does not need to maintain any costly appearance models and is therefore appropriate for resource constraint ego-vision systems. The proposed segmentation combined with skin cues is validated by a novel application on authenticating hand-gestured signature captured by wearable cameras. The third method combines both motion and appearance. Foreground probabilities are jointly estimated by motion and appearance. After the mega-pixel denoising process, the probability estimates and gradient image are combined by Graph-Cut to produce the segmentation mask. This method is universal as it can handle all types of moving cameras. Background Subtraction Foreground Segmentation Freely Moving Cameras Pan-Tilt-Zoom Ego-motion Compensation Mega-Pixels Signal Processing
7	Active Regulation of Speed During a Simulated Low-altitude Flight Task: Altitude Matters! Bennett, April M. 27 December 2006 (has links) No description available. Psychology, Experimental Motion Processing Self-Motion Ego-Motion Global Optical Flow Rate Speed Judgments Active Regulation of Speed Low-Altitude Flight
8	HANDHELD LIDAR ODOMETRY ESTIMATION AND MAPPING SYSTEM Holmqvist, Niclas January 2018 (has links) Ego-motion sensors are commonly used for pose estimation in Simultaneous Localization And Mapping (SLAM) algorithms. Inertial Measurement Units (IMUs) are popular sensors but suffer from integration drift over longer time scales. To remedy the drift they are often used in combination with additional sensors, such as a LiDAR. Pose estimation is used when scans, produced by these additional sensors, are being matched. The matching of scans can be computationally heavy as one scan can contain millions of data points. Methods exist to simplify the problem of finding the relative pose between sensor data, such as the Normal Distribution Transform SLAM algorithm. The algorithm separates the point cloud data into a voxelgrid and represent each voxel as a normal distribution, effectively decreasing the amount of data points. Registration is based on a function which converges to a minimum. Sub-optimal conditions can cause the function to converge at a local minimum. To remedy this problem this thesis explores the benefits of combining IMU sensor data to estimate the pose to be used in the NDT SLAM algorithm. SLAM NDT Normal Distribution Transform Robotics IMU LiDAR GPS RTK-GPS ego-motion pose estimation Embedded Systems Inbäddad systemteknik Robotics Robotteknik och automation Signal Processing Signalbehandling
9	An Efficient Feature Descriptor and Its Real-Time Applications Desai, Alok 01 June 2015 (has links) (PDF) Finding salient features in an image, and matching them to their corresponding features in another image is an important step for many vision-based applications. Feature description plays an important role in the feature matching process. A robust feature descriptor must works with a number of image deformations and should be computationally efficient. For resource-limited systems, floating point and complex operations such as multiplication and square root are not desirable. This research first introduces a robust and efficient feature descriptor called PRObability (PRO) descriptor that meets these requirements without sacrificing matching accuracy. The PRO descriptor is further improved by incorporating only affine features for matching. While performing well, PRO descriptor still requires larger descriptor size, higher offline computation time, and more memory space than other binary feature descriptors. SYnthetic BAsis (SYBA) descriptor is developed to overcome these drawbacks. SYBA is built on the basis of a new compressed sensing theory that uses synthetic basis functions to uniquely encode or reconstruct a signal. The SYBA descriptor is designed to provide accurate feature matching for real-time vision applications. To demonstrate its performance, we develop algorithms that utilize SYBA descriptor to localize the soccer ball in a broadcast soccer game video, track ground objects for unmanned aerial vehicle, and perform motion analysis, and improve visual odometry accuracy for advanced driver assistance systems. SYBA provides high feature matching accuracy with computational simplicity and requires minimal computational resources. It is a hardware-friendly feature description and matching algorithm suitable for embedded vision applications. Feature detection feature description feature matching object tracking soccer ball game visual odometry ego-motion drift reduction computer vision Electrical and Computer Engineering
10	Through the Blur with Deep Learning : A Comparative Study Assessing Robustness in Visual Odometry Techniques Berglund, Alexander January 2023 (has links) In this thesis, the robustness of deep learning techniques in the field of visual odometry is investigated, with a specific focus on the impact of motion blur. A comparative study is conducted, evaluating the performance of state-of-the-art deep convolutional neural network methods, namely DF-VO and DytanVO, against ORB-SLAM3, a well-established non-deep-learning technique for visual simultaneous localization and mapping. The objective is to quantitatively assess the performance of these models as a function of motion blur. The evaluation is carried out on a custom synthetic dataset, which simulates a camera navigating through a forest environment. The dataset includes trajectories with varying degrees of motion blur, caused by camera translation, and optionally, pitch and yaw rotational noise. The results demonstrate that deep learning-based methods maintained robust performance despite the challenging conditions presented in the test data, while excessive blur lead to tracking failures in the geometric model. This suggests that the ability of deep neural network architectures to automatically learn hierarchical feature representations and capture complex, abstract features may enhance the robustness of deep learning-based visual odometry techniques in challenging conditions, compared to their geometric counterparts. artificial intelligence AI machine learning ML deep learning DL computer vision neural networks NN convolutional neural networks CNN visual odometry VO robustness motion blur AirForestry localization navigation ego-motion pose estimation SLAM DF-VO DytanVO ORB-SLAM3 artificiell intelligens maskininlärning datorseende Computer Sciences Datavetenskap (datalogi)

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