39,909 research outputs found
Independent Motion Detection with Event-driven Cameras
Unlike standard cameras that send intensity images at a constant frame rate,
event-driven cameras asynchronously report pixel-level brightness changes,
offering low latency and high temporal resolution (both in the order of
micro-seconds). As such, they have great potential for fast and low power
vision algorithms for robots. Visual tracking, for example, is easily achieved
even for very fast stimuli, as only moving objects cause brightness changes.
However, cameras mounted on a moving robot are typically non-stationary and the
same tracking problem becomes confounded by background clutter events due to
the robot ego-motion. In this paper, we propose a method for segmenting the
motion of an independently moving object for event-driven cameras. Our method
detects and tracks corners in the event stream and learns the statistics of
their motion as a function of the robot's joint velocities when no
independently moving objects are present. During robot operation, independently
moving objects are identified by discrepancies between the predicted corner
velocities from ego-motion and the measured corner velocities. We validate the
algorithm on data collected from the neuromorphic iCub robot. We achieve a
precision of ~ 90 % and show that the method is robust to changes in speed of
both the head and the target.Comment: 7 pages, 6 figure
DroTrack: High-speed Drone-based Object Tracking Under Uncertainty
We present DroTrack, a high-speed visual single-object tracking framework for
drone-captured video sequences. Most of the existing object tracking methods
are designed to tackle well-known challenges, such as occlusion and cluttered
backgrounds. The complex motion of drones, i.e., multiple degrees of freedom in
three-dimensional space, causes high uncertainty. The uncertainty problem leads
to inaccurate location predictions and fuzziness in scale estimations. DroTrack
solves such issues by discovering the dependency between object representation
and motion geometry. We implement an effective object segmentation based on
Fuzzy C Means (FCM). We incorporate the spatial information into the membership
function to cluster the most discriminative segments. We then enhance the
object segmentation by using a pre-trained Convolution Neural Network (CNN)
model. DroTrack also leverages the geometrical angular motion to estimate a
reliable object scale. We discuss the experimental results and performance
evaluation using two datasets of 51,462 drone-captured frames. The combination
of the FCM segmentation and the angular scaling increased DroTrack precision by
up to and decreased the centre location error by pixels on average.
DroTrack outperforms all the high-speed trackers and achieves comparable
results in comparison to deep learning trackers. DroTrack offers high frame
rates up to 1000 frame per second (fps) with the best location precision, more
than a set of state-of-the-art real-time trackers.Comment: 10 pages, 12 figures, FUZZ-IEEE 202
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
Visual 3-D SLAM from UAVs
The aim of the paper is to present, test and discuss the implementation of Visual SLAM techniques to images taken from Unmanned Aerial Vehicles (UAVs) outdoors, in partially structured environments. Every issue of the whole process is discussed in order to obtain more accurate localization and mapping from UAVs flights. Firstly, the issues related to the visual features of objects in the scene, their distance to the UAV, and the related image acquisition system and their calibration are evaluated for improving the whole process. Other important, considered issues are related to the image processing techniques, such as interest point detection, the matching procedure and the scaling factor. The whole system has been tested using the COLIBRI mini UAV in partially structured environments. The results that have been obtained for localization, tested against the GPS information of the flights, show that Visual SLAM delivers reliable localization and mapping that makes it suitable for some outdoors applications when flying UAVs
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