121,599 research outputs found
Evaluation of machine vision techniques for use within flight control systems
In this thesis, two of the main technical limitations for a massive deployment of Unmanned Aerial Vehicle (UAV) have been considered.;The Aerial Refueling problem is analyzed in the first section. A solution based on the integration of \u27conventional\u27 GPS/INS and Machine Vision sensor is proposed with the purpose of measuring the relative distance between a refueling tanker and UAV. In this effort, comparisons between Point Matching (PM) algorithms and Pose Estimation (PE) algorithms have been developed in order to improve the performance of the Machine Vision sensor. A method of integration based on Extended Kalman Filter (EKF) between GPS/INS and Machine Vision system is also developed with the goal of reducing the tracking error in the \u27pre-contact\u27 to contact and refueling phases.;In the second section of the thesis the issue of Collision Identification (CI) is addressed. A proposed solution consists on the use of Optical Flow (OF) algorithms for the detection of possible collisions in the range of vision of a single camera. The effort includes a study of the performance of different Optical Flow algorithms in different scenarios as well as a method to compute the ideal optical flow with the aim of evaluating the algorithms. An analysis on the suitability for a future real time implementation is also performed for all the analyzed algorithms.;Results of the tests show that the Machine Vision technology can be used to improve the performance in the Aerial Refueling problem. In the Collision Identification problem, the Machine Vision has to be integrated with standard sensors in order to be used inside the Flight Control System
FPGA-Based Multimodal Embedded Sensor System Integrating Low- and Mid-Level Vision
Motion estimation is a low-level vision task that is especially relevant due to its wide range of applications in the real world. Many of the best motion estimation algorithms include some of the features that are found in mammalians, which would demand huge computational resources and therefore are not usually available in real-time. In this paper we present a novel bioinspired sensor based on the synergy between optical flow and orthogonal variant moments. The bioinspired sensor has been designed for Very Large Scale Integration (VLSI) using properties of the mammalian cortical motion pathway. This sensor combines low-level primitives (optical flow and image moments) in order to produce a mid-level vision abstraction layer. The results are described trough experiments showing the validity of the proposed system and an analysis of the computational resources and performance of the applied algorithms
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An evaluation framework for stereo-based driver assistance
This is the post-print version of the Article - Copyright @ 2012 Springer VerlagThe accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury
database. However, equivalent data for automotive or robotics applications
rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while
circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases.
Using our framework we show examples on several types of ground truthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on
pixel and object level. In more detail we evaluate an intermediate representation
called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the StixelWorld vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km
Sparse optical flow regularisation for real-time visual tracking
Optical flow can greatly improve the robustness of visual tracking algorithms. While dense optical flow algorithms have various applications, they can not be used for real-time solutions without resorting to GPU calculations. Furthermore, most optical flow algorithms fail in challenging lighting environments due to the violation of the brightness constraint. We propose a simple but effective iterative regularisation scheme for real-time, sparse optical flow algorithms, that is shown to be robust to sudden illumination changes and can handle large displacements. The algorithm proves to outperform well known techniques in real life video sequences, while being much faster to calculate. Our solution increases the robustness of a real-time particle filter based tracking application, consuming only a fraction of the available CPU power. Furthermore, a new and realistic optical flow dataset with annotated ground truth is created and made freely available for research purposes
A Fusion Approach for Multi-Frame Optical Flow Estimation
To date, top-performing optical flow estimation methods only take pairs of
consecutive frames into account. While elegant and appealing, the idea of using
more than two frames has not yet produced state-of-the-art results. We present
a simple, yet effective fusion approach for multi-frame optical flow that
benefits from longer-term temporal cues. Our method first warps the optical
flow from previous frames to the current, thereby yielding multiple plausible
estimates. It then fuses the complementary information carried by these
estimates into a new optical flow field. At the time of writing, our method
ranks first among published results in the MPI Sintel and KITTI 2015
benchmarks. Our models will be available on https://github.com/NVlabs/PWC-Net.Comment: Work accepted at IEEE Winter Conference on Applications of Computer
Vision (WACV 2019
Search Tracker: Human-derived object tracking in-the-wild through large-scale search and retrieval
Humans use context and scene knowledge to easily localize moving objects in
conditions of complex illumination changes, scene clutter and occlusions. In
this paper, we present a method to leverage human knowledge in the form of
annotated video libraries in a novel search and retrieval based setting to
track objects in unseen video sequences. For every video sequence, a document
that represents motion information is generated. Documents of the unseen video
are queried against the library at multiple scales to find videos with similar
motion characteristics. This provides us with coarse localization of objects in
the unseen video. We further adapt these retrieved object locations to the new
video using an efficient warping scheme. The proposed method is validated on
in-the-wild video surveillance datasets where we outperform state-of-the-art
appearance-based trackers. We also introduce a new challenging dataset with
complex object appearance changes.Comment: Under review with the IEEE Transactions on Circuits and Systems for
Video Technolog
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