2,864 research outputs found
Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras
We propose a new method to estimate the 6-dof trajectory of a flying object
such as a quadrotor UAV within a 3D airspace monitored using multiple fixed
ground cameras. It is based on a new structure from motion formulation for the
3D reconstruction of a single moving point with known motion dynamics. Our main
contribution is a new bundle adjustment procedure which in addition to
optimizing the camera poses, regularizes the point trajectory using a prior
based on motion dynamics (or specifically flight dynamics). Furthermore, we can
infer the underlying control input sent to the UAV's autopilot that determined
its flight trajectory.
Our method requires neither perfect single-view tracking nor appearance
matching across views. For robustness, we allow the tracker to generate
multiple detections per frame in each video. The true detections and the data
association across videos is estimated using robust multi-view triangulation
and subsequently refined during our bundle adjustment procedure. Quantitative
evaluation on simulated data and experiments on real videos from indoor and
outdoor scenes demonstrates the effectiveness of our method
Smart environment monitoring through micro unmanned aerial vehicles
In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection
Road User Detection in Videos
Successive frames of a video are highly redundant, and the most popular
object detection methods do not take advantage of this fact. Using multiple
consecutive frames can improve detection of small objects or difficult examples
and can improve speed and detection consistency in a video sequence, for
instance by interpolating features between frames. In this work, a novel
approach is introduced to perform online video object detection using two
consecutive frames of video sequences involving road users. Two new models,
RetinaNet-Double and RetinaNet-Flow, are proposed, based respectively on the
concatenation of a target frame with a preceding frame, and the concatenation
of the optical flow with the target frame. The models are trained and evaluated
on three public datasets. Experiments show that using a preceding frame
improves performance over single frame detectors, but using explicit optical
flow usually does not
Road User Detection in Videos
Successive frames of a video are highly redundant, and the most popular
object detection methods do not take advantage of this fact. Using multiple
consecutive frames can improve detection of small objects or difficult examples
and can improve speed and detection consistency in a video sequence, for
instance by interpolating features between frames. In this work, a novel
approach is introduced to perform online video object detection using two
consecutive frames of video sequences involving road users. Two new models,
RetinaNet-Double and RetinaNet-Flow, are proposed, based respectively on the
concatenation of a target frame with a preceding frame, and the concatenation
of the optical flow with the target frame. The models are trained and evaluated
on three public datasets. Experiments show that using a preceding frame
improves performance over single frame detectors, but using explicit optical
flow usually does not
Vision and Learning for Deliberative Monocular Cluttered Flight
Cameras provide a rich source of information while being passive, cheap and
lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work
we present the first implementation of receding horizon control, which is
widely used in ground vehicles, with monocular vision as the only sensing mode
for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a
number of contributions: novel coupling of perception and control via relevant
and diverse, multiple interpretations of the scene around the robot, leveraging
recent advances in machine learning to showcase anytime budgeted cost-sensitive
feature selection, and fast non-linear regression for monocular depth
prediction. We empirically demonstrate the efficacy of our novel pipeline via
real world experiments of more than 2 kms through dense trees with a quadrotor
built from off-the-shelf parts. Moreover our pipeline is designed to combine
information from other modalities like stereo and lidar as well if available
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