Essays about: "Monocular"

Showing result 41 - 45 of 77 essays containing the word Monocular.

  1. 41. Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms

    University essay from Linköpings universitet/Reglerteknik

    Author : Albin Vestin; Gustav Strandberg; [2019]
    Keywords : evaluation; target tracking; multiple sensors; non-causal; smoother; smoothing; tracking; vehicle tracking; camera; lidar; estimate; estimation; prediction; vehicle dynamics; sensor fusion; real-time tracking; extended kalman filter; filter validation; validation; position estimation; velocity estimation; dynamic model; model complexity; multi object tracking; multiple object; tracking; single object tracking; data association; tracking fundamentals; iterated kalman filter; track management; gnn; global nearest neighbour; mahalanobis; mahalanobis distance; performance evaluation; differential gps; dgps; roi; ego; several sensors; sensors; rmse; root mean square error; invertible motion; anti-causal motion; anti-causal tracking; constant velocity; gnn; imu; tfs; two filter smoother; ekf; rts; radar; inertial measurement unit; nonlinear; nonlinear systems; mono camera; monocular camera; noise model; tracking performance; fixed interval smoothing; m n logic; centralized fusion; non-causal object tracker; car tracking; car dynamics; automotive; active safety; object tracking; automotive industry; thesis; master; reverse dynamics; reverse tracking; reverse sequence; sequence tracking; data propagation; ground truth; estimating ground truth; additional sensors; mounted sensors; true estimates; environment; comparison; algorithm; independent targets; overlapping; measurements; occluded; track switch; improve; lower; uncertainty; more; certain; state; process; noise; covariance; sampling; image; sprt; adas; cnn; cv; pdf; track; target; ego; tracker; tentative track; observatiom; online tracking; offline tracking; online; offline; recorded; sequences; robust; self driving; self-driving; car; traffic; trajectory; true state; scenario; scenarios; future; accurate; output; advanced; driver; assistance; systems; non-linear; complex noise; pedestrian; truck; bus; maneuvering; vehicles; processed; measurement; frame; state; correction; probability; density; function; tuning; likelihood; transition; measurement; motion; model; recursion; gaussian; approximation; distribution; linear; jacobian; multiplicative; noise; ratio; ad; hoc; ad hoc; state; space; approach; backward; auction; euclidean; distance; statistical; threshold; gating; association; margin; normalize; covariance; matrix; fusion; confirmed; rejected; tentative; history; absolute; error; modular; ego motion; parameters; variables; logg; hardware; specification; fused; causal; factorization; independent; uncorrelated; transform; moving; rotation; translation; oncoming; overtaking;

    Abstract : Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. READ MORE

  3. 42. Monocular Depth Estimation Using Deep Convolutional Neural Networks

    University essay from Linköpings universitet/Datorseende

    Author : Susanna Larsson; [2019]
    Keywords : Depth estimation; depth maps; monocular SLAM; mono-SLAM; pixelwise depth prediction; encoder-decoder network;

    Abstract : For a long time stereo-cameras have been deployed in visual Simultaneous Localization And Mapping (SLAM) systems to gain 3D information. Even though stereo-cameras show good performance, the main disadvantage is the complex and expensive hardware setup it requires, which limits the use of the system. READ MORE

  4. 43. Improving the Accuracy of 2D On-Road Object Detection Based on Deep Learning Techniques

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Ying Yu; [2018]
    Keywords : ;

    Abstract : This paper focuses on improving the accuracy of detecting on-road objects, includingcars, trucks, pedestrians, and cyclists. To meet the requirements of theembedded vision system and maintain a high speed of detection in the advanceddriving assistance system (ADAS) domain, the neural network model is designedbased on single channel images as input from a monocular camera. READ MORE

  5. 44. Monocular Visual Odometry for Underwater Navigation : An examination of the performance of two methods

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Maxime Voisin-Denoual; [2018]
    Keywords : Visual Odometry; Orbslam; AUV; autonomous underwater vehicle; monocular;

    Abstract : This thesis examines two methods for monocular visual odometry, FAST + KLT and ORBSLAM2, in the case of underwater environments.This is done by implementing and testing the methods on different underwater datasets. The results for the FAST + KLT provide no evidence that this method is effective in underwater settings. READ MORE

  6. 45. Improving deep monocular depth predictions using dense narrow field of view depth images

    University essay from KTH/Robotik, perception och lärande, RPL

    Author : Christoffer Möckelind; [2018]
    Keywords : Deep learning; Monocular; Depth estimation; Narrow field of view; RGB; RGBD; Noicy depth; Dense depth; Narrow depth; Sparse depth;

    Abstract : In this work we study a depth prediction problem where we provide a narrow field of view depth image and a wide field of view RGB image to a deep network tasked with predicting the depth for the entire RGB image. We show that by providing a narrow field of view depth image, we improve results for the area outside the provided depth compared to an earlier approach only utilizing a single RGB image for depth prediction. READ MORE