2,207 research outputs found
Multi-object Tracking in Aerial Image Sequences using Aerial Tracking Learning and Detection Algorithm
Vison based tracking in aerial images has its own significance in the areas of both civil and defense applications. A novel algorithm called aerial tracking learning detection which works on the basis of the popular tracking learning detection algorithm to effectively track single and multiple objects in aerial images is proposed in this study. Tracking learning detection (TLD) considers both appearance and motion features for tracking. It can handle occlusion to certain extent, and can work well on long duration video sequences. However, when objects are tracked in aerial images taken from platforms like unmanned air vehicle, the problems of frequent pose change, scale and illumination variations arise adding to low resolution, noise and jitter introduced by motion of the camera. The proposed algorithm incorporates compensation for the camera movement, algorithmic modifications in combining appearance and motion cues for detection and tracking of multiple objects and enhancements in the form of inter object distance measure for improved performance of the tracker when there are many identical objects in proximity. This algorithm has been tested on a large number of aerial sequences including benchmark videos, TLD dataset and many classified unmanned air vehicle sequences and has shown better performance in comparison to TLD.
Deep Drone Racing: From Simulation to Reality with Domain Randomization
Dynamically changing environments, unreliable state estimation, and operation
under severe resource constraints are fundamental challenges that limit the
deployment of small autonomous drones. We address these challenges in the
context of autonomous, vision-based drone racing in dynamic environments. A
racing drone must traverse a track with possibly moving gates at high speed. We
enable this functionality by combining the performance of a state-of-the-art
planning and control system with the perceptual awareness of a convolutional
neural network (CNN). The resulting modular system is both platform- and
domain-independent: it is trained in simulation and deployed on a physical
quadrotor without any fine-tuning. The abundance of simulated data, generated
via domain randomization, makes our system robust to changes of illumination
and gate appearance. To the best of our knowledge, our approach is the first to
demonstrate zero-shot sim-to-real transfer on the task of agile drone flight.
We extensively test the precision and robustness of our system, both in
simulation and on a physical platform, and show significant improvements over
the state of the art.Comment: Accepted as a Regular Paper to the IEEE Transactions on Robotics
Journal. arXiv admin note: substantial text overlap with arXiv:1806.0854
Visual Servoing from Deep Neural Networks
We present a deep neural network-based method to perform high-precision,
robust and real-time 6 DOF visual servoing. The paper describes how to create a
dataset simulating various perturbations (occlusions and lighting conditions)
from a single real-world image of the scene. A convolutional neural network is
fine-tuned using this dataset to estimate the relative pose between two images
of the same scene. The output of the network is then employed in a visual
servoing control scheme. The method converges robustly even in difficult
real-world settings with strong lighting variations and occlusions.A
positioning error of less than one millimeter is obtained in experiments with a
6 DOF robot.Comment: fixed authors lis
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