14,660 research outputs found
Online Tracking Parameter Adaptation based on Evaluation
Parameter tuning is a common issue for many tracking algorithms. In order to
solve this problem, this paper proposes an online parameter tuning to adapt a
tracking algorithm to various scene contexts. In an offline training phase,
this approach learns how to tune the tracker parameters to cope with different
contexts. In the online control phase, once the tracking quality is evaluated
as not good enough, the proposed approach computes the current context and
tunes the tracking parameters using the learned values. The experimental
results show that the proposed approach improves the performance of the
tracking algorithm and outperforms recent state of the art trackers. This paper
brings two contributions: (1) an online tracking evaluation, and (2) a method
to adapt online tracking parameters to scene contexts.Comment: IEEE International Conference on Advanced Video and Signal-based
Surveillance (2013
Object Referring in Videos with Language and Human Gaze
We investigate the problem of object referring (OR) i.e. to localize a target
object in a visual scene coming with a language description. Humans perceive
the world more as continued video snippets than as static images, and describe
objects not only by their appearance, but also by their spatio-temporal context
and motion features. Humans also gaze at the object when they issue a referring
expression. Existing works for OR mostly focus on static images only, which
fall short in providing many such cues. This paper addresses OR in videos with
language and human gaze. To that end, we present a new video dataset for OR,
with 30, 000 objects over 5, 000 stereo video sequences annotated for their
descriptions and gaze. We further propose a novel network model for OR in
videos, by integrating appearance, motion, gaze, and spatio-temporal context
into one network. Experimental results show that our method effectively
utilizes motion cues, human gaze, and spatio-temporal context. Our method
outperforms previousOR methods. For dataset and code, please refer
https://people.ee.ethz.ch/~arunv/ORGaze.html.Comment: Accepted to CVPR 2018, 10 pages, 6 figure
The Evolution of First Person Vision Methods: A Survey
The emergence of new wearable technologies such as action cameras and
smart-glasses has increased the interest of computer vision scientists in the
First Person perspective. Nowadays, this field is attracting attention and
investments of companies aiming to develop commercial devices with First Person
Vision recording capabilities. Due to this interest, an increasing demand of
methods to process these videos, possibly in real-time, is expected. Current
approaches present a particular combinations of different image features and
quantitative methods to accomplish specific objectives like object detection,
activity recognition, user machine interaction and so on. This paper summarizes
the evolution of the state of the art in First Person Vision video analysis
between 1997 and 2014, highlighting, among others, most commonly used features,
methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart
Glasses, Computer Vision, Video Analytics, Human-machine Interactio
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