5,796 research outputs found
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
A neural circuit for navigation inspired by C. elegans Chemotaxis
We develop an artificial neural circuit for contour tracking and navigation
inspired by the chemotaxis of the nematode Caenorhabditis elegans. In order to
harness the computational advantages spiking neural networks promise over their
non-spiking counterparts, we develop a network comprising 7-spiking neurons
with non-plastic synapses which we show is extremely robust in tracking a range
of concentrations. Our worm uses information regarding local temporal gradients
in sodium chloride concentration to decide the instantaneous path for foraging,
exploration and tracking. A key neuron pair in the C. elegans chemotaxis
network is the ASEL & ASER neuron pair, which capture the gradient of
concentration sensed by the worm in their graded membrane potentials. The
primary sensory neurons for our network are a pair of artificial spiking
neurons that function as gradient detectors whose design is adapted from a
computational model of the ASE neuron pair in C. elegans. Simulations show that
our worm is able to detect the set-point with approximately four times higher
probability than the optimal memoryless Levy foraging model. We also show that
our spiking neural network is much more efficient and noise-resilient while
navigating and tracking a contour, as compared to an equivalent non-spiking
network. We demonstrate that our model is extremely robust to noise and with
slight modifications can be used for other practical applications such as
obstacle avoidance. Our network model could also be extended for use in
three-dimensional contour tracking or obstacle avoidance
Neuromorphic event-based slip detection and suppression in robotic grasping and manipulation
Slip detection is essential for robots to make robust grasping and fine
manipulation. In this paper, a novel dynamic vision-based finger system for
slip detection and suppression is proposed. We also present a baseline and
feature based approach to detect object slips under illumination and vibration
uncertainty. A threshold method is devised to autonomously sample noise in
real-time to improve slip detection. Moreover, a fuzzy based suppression
strategy using incipient slip feedback is proposed for regulating the grip
force. A comprehensive experimental study of our proposed approaches under
uncertainty and system for high-performance precision manipulation are
presented. We also propose a slip metric to evaluate such performance
quantitatively. Results indicate that the system can effectively detect
incipient slip events at a sampling rate of 2kHz () and
suppress them before a gross slip occurs. The event-based approach holds
promises to high precision manipulation task requirement in industrial
manufacturing and household services.Comment: 18 pages, 14 figure
Feature Based Multi View Image Registration by Detecting the Feature with Fuzzy Logic for Corner Detection
This paper aim to Present accurate feature base registration by detecting the feature with Fuzzy logic for corner detection. Image registration is process used to match two or more partially overlapping image taken for example at different times ,from different sensors, or from different viewpoints and stitch these image into one panoramic image comprising whole scene. It is a fundamental image processing technique very useful in integrating information from different sensors, finding changes in image taken at different time, inferring three-dimensional information from stereo images and recognizing model-based objects. The paper presents a corner detection algorithm for feature detection which employs such fuzzy reasoning. The robustness of the proposed algorithm is compared to well-known conventional Harris corner detectors and its performance is also tested over a noise image.
DOI: 10.17762/ijritcc2321-8169.150616
A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method
In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCA-KNN) approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform) descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class). The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods
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