4,468 research outputs found
Deep Motion Features for Visual Tracking
Robust visual tracking is a challenging computer vision problem, with many
real-world applications. Most existing approaches employ hand-crafted
appearance features, such as HOG or Color Names. Recently, deep RGB features
extracted from convolutional neural networks have been successfully applied for
tracking. Despite their success, these features only capture appearance
information. On the other hand, motion cues provide discriminative and
complementary information that can improve tracking performance. Contrary to
visual tracking, deep motion features have been successfully applied for action
recognition and video classification tasks. Typically, the motion features are
learned by training a CNN on optical flow images extracted from large amounts
of labeled videos.
This paper presents an investigation of the impact of deep motion features in
a tracking-by-detection framework. We further show that hand-crafted, deep RGB,
and deep motion features contain complementary information. To the best of our
knowledge, we are the first to propose fusing appearance information with deep
motion features for visual tracking. Comprehensive experiments clearly suggest
that our fusion approach with deep motion features outperforms standard methods
relying on appearance information alone.Comment: ICPR 2016. Best paper award in the "Computer Vision and Robot Vision"
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Self-Selective Correlation Ship Tracking Method for Smart Ocean System
In recent years, with the development of the marine industry, navigation
environment becomes more complicated. Some artificial intelligence
technologies, such as computer vision, can recognize, track and count the
sailing ships to ensure the maritime security and facilitates the management
for Smart Ocean System. Aiming at the scaling problem and boundary effect
problem of traditional correlation filtering methods, we propose a
self-selective correlation filtering method based on box regression (BRCF). The
proposed method mainly include: 1) A self-selective model with negative samples
mining method which effectively reduces the boundary effect in strengthening
the classification ability of classifier at the same time; 2) A bounding box
regression method combined with a key points matching method for the scale
prediction, leading to a fast and efficient calculation. The experimental
results show that the proposed method can effectively deal with the problem of
ship size changes and background interference. The success rates and precisions
were higher than Discriminative Scale Space Tracking (DSST) by over 8
percentage points on the marine traffic dataset of our laboratory. In terms of
processing speed, the proposed method is higher than DSST by nearly 22 Frames
Per Second (FPS)
Vision-Based Production of Personalized Video
In this paper we present a novel vision-based system for the automated production of personalised video souvenirs for visitors in leisure and cultural heritage venues. Visitors are visually identified and tracked through a camera network. The system produces a personalized DVD souvenir at the end of a visitor’s stay allowing visitors to relive their experiences. We analyze how we identify visitors by fusing facial and body features, how we track visitors, how the tracker recovers from failures due to occlusions, as well as how we annotate and compile the final product. Our experiments demonstrate the feasibility of the proposed approach
A Differential Approach for Gaze Estimation
Non-invasive gaze estimation methods usually regress gaze directions directly
from a single face or eye image. However, due to important variabilities in eye
shapes and inner eye structures amongst individuals, universal models obtain
limited accuracies and their output usually exhibit high variance as well as
biases which are subject dependent. Therefore, increasing accuracy is usually
done through calibration, allowing gaze predictions for a subject to be mapped
to his/her actual gaze. In this paper, we introduce a novel image differential
method for gaze estimation. We propose to directly train a differential
convolutional neural network to predict the gaze differences between two eye
input images of the same subject. Then, given a set of subject specific
calibration images, we can use the inferred differences to predict the gaze
direction of a novel eye sample. The assumption is that by allowing the
comparison between two eye images, annoyance factors (alignment, eyelid
closing, illumination perturbations) which usually plague single image
prediction methods can be much reduced, allowing better prediction altogether.
Experiments on 3 public datasets validate our approach which constantly
outperforms state-of-the-art methods even when using only one calibration
sample or when the latter methods are followed by subject specific gaze
adaptation.Comment: Extension to our paper A differential approach for gaze estimation
with calibration (BMVC 2018) Submitted to PAMI on Aug. 7th, 2018 Accepted by
PAMI short on Dec. 2019, in IEEE Transactions on Pattern Analysis and Machine
Intelligenc
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