73,203 research outputs found
DART: Distribution Aware Retinal Transform for Event-based Cameras
We introduce a generic visual descriptor, termed as distribution aware
retinal transform (DART), that encodes the structural context using log-polar
grids for event cameras. The DART descriptor is applied to four different
problems, namely object classification, tracking, detection and feature
matching: (1) The DART features are directly employed as local descriptors in a
bag-of-features classification framework and testing is carried out on four
standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS,
NCaltech-101). (2) Extending the classification system, tracking is
demonstrated using two key novelties: (i) For overcoming the low-sample problem
for the one-shot learning of a binary classifier, statistical bootstrapping is
leveraged with online learning; (ii) To achieve tracker robustness, the scale
and rotation equivariance property of the DART descriptors is exploited for the
one-shot learning. (3) To solve the long-term object tracking problem, an
object detector is designed using the principle of cluster majority voting. The
detection scheme is then combined with the tracker to result in a high
intersection-over-union score with augmented ground truth annotations on the
publicly available event camera dataset. (4) Finally, the event context encoded
by DART greatly simplifies the feature correspondence problem, especially for
spatio-temporal slices far apart in time, which has not been explicitly tackled
in the event-based vision domain.Comment: 12 pages, revision submitted to TPAMI in Nov 201
LDSO: Direct Sparse Odometry with Loop Closure
In this paper we present an extension of Direct Sparse Odometry (DSO) to a
monocular visual SLAM system with loop closure detection and pose-graph
optimization (LDSO). As a direct technique, DSO can utilize any image pixel
with sufficient intensity gradient, which makes it robust even in featureless
areas. LDSO retains this robustness, while at the same time ensuring
repeatability of some of these points by favoring corner features in the
tracking frontend. This repeatability allows to reliably detect loop closure
candidates with a conventional feature-based bag-of-words (BoW) approach. Loop
closure candidates are verified geometrically and Sim(3) relative pose
constraints are estimated by jointly minimizing 2D and 3D geometric error
terms. These constraints are fused with a co-visibility graph of relative poses
extracted from DSO's sliding window optimization. Our evaluation on publicly
available datasets demonstrates that the modified point selection strategy
retains the tracking accuracy and robustness, and the integrated pose-graph
optimization significantly reduces the accumulated rotation-, translation- and
scale-drift, resulting in an overall performance comparable to state-of-the-art
feature-based systems, even without global bundle adjustment
Follow me travel bag
Follow Me Travel Bag is basically a smart bag to be used by travelers in away that provide them with additional features the normal travel bag does not. This bag will be empowered by a built-in tracking system that provide automatic self-control over the bag. It will integrate modern technology to provide easier usage of a travel bag, and enhance the security and movement issues. The main objective of this project is to ease the travel experience of individuals in handling their travel bags throughout their movement. This is accomplished by firstly making the bag following its owner without a need to drag it. Secondly, the bag will contain a location finder system to overcome the possibility of being lost, forgotten or stolen. This will solve the problem of losing the bag forever among with its contents which are valuable in much cases. This research is investigating the most suitable approach to achieve these targets though designing, controlling and testing of a smart programmable tracking system inserted in a travel bag
Object Tracking with Multiple Instance Learning and Gaussian Mixture Model
Recently, Multiple Instance Learning (MIL) technique has been introduced for object tracking\linebreak applications, which has shown its good performance to handle drifting problem. While some instances in positive bags not only contain objects, but also contain the background, it is not reliable to simply assume that each feature of instances in positive bags obeys a single Gaussian distribution. In this paper, a tracker based on online multiple instance boosting has been developed, which employs Gaussian Mixture Model (GMM) and single Gaussian distribution respectively to model features of instances in positive and negative bags. The differences between samples and the model are integrated into the process of updating the parameters for GMM. With the Haar-like features extracted from the bags, a set of weak classifiers are trained to construct a strong classifier, which is used to track the object location at a new frame. And the classifier can be updated online frame by frame. Experimental results have shown that our tracker is more stable and efficient when dealing with the illumination, rotation, pose and appearance changes
Gaze Embeddings for Zero-Shot Image Classification
Zero-shot image classification using auxiliary information, such as
attributes describing discriminative object properties, requires time-consuming
annotation by domain experts. We instead propose a method that relies on human
gaze as auxiliary information, exploiting that even non-expert users have a
natural ability to judge class membership. We present a data collection
paradigm that involves a discrimination task to increase the information
content obtained from gaze data. Our method extracts discriminative descriptors
from the data and learns a compatibility function between image and gaze using
three novel gaze embeddings: Gaze Histograms (GH), Gaze Features with Grid
(GFG) and Gaze Features with Sequence (GFS). We introduce two new
gaze-annotated datasets for fine-grained image classification and show that
human gaze data is indeed class discriminative, provides a competitive
alternative to expert-annotated attributes, and outperforms other baselines for
zero-shot image classification
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