1,470 research outputs found
SANet: Structure-Aware Network for Visual Tracking
Convolutional neural network (CNN) has drawn increasing interest in visual
tracking owing to its powerfulness in feature extraction. Most existing
CNN-based trackers treat tracking as a classification problem. However, these
trackers are sensitive to similar distractors because their CNN models mainly
focus on inter-class classification. To address this problem, we use
self-structure information of object to distinguish it from distractors.
Specifically, we utilize recurrent neural network (RNN) to model object
structure, and incorporate it into CNN to improve its robustness to similar
distractors. Considering that convolutional layers in different levels
characterize the object from different perspectives, we use multiple RNNs to
model object structure in different levels respectively. Extensive experiments
on three benchmarks, OTB100, TC-128 and VOT2015, show that the proposed
algorithm outperforms other methods. Code is released at
http://www.dabi.temple.edu/~hbling/code/SANet/SANet.html.Comment: In CVPR Deep Vision Workshop, 201
Unmanned aerial vehicle video-based target tracking algorithm Using sparse representation
Target tracking based on unmanned aerial vehicle
(UAV) video is a significant technique in intelligent urban
surveillance systems for smart city applications, such as smart
transportation, road traffic monitoring, inspection of stolen
vehicle, etc. In this paper, a vision-based target tracking algorithm
aiming at locating UAV-captured targets, like pedestrian and
vehicle, is proposed using sparse representation theory. First of all,
each target candidate is sparsely represented in the subspace
spanned by a joint dictionary. Then, the sparse representation
coefficient is further constrained by an L2 regularization based on
the temporal consistency. To cope with the partial occlusion
appearing in UAV videos, a Markov Random Field (MRF)-based
binary support vector with contiguous occlusion constraint is
introduced to our sparse representation model. For long-term
tracking, the particle filter framework along with a dynamic
template update scheme is designed. Both qualitative and
quantitative experiments implemented on visible (Vis) and
infrared (IR) UAV videos prove that the presented tracker can
achieve better performances in terms of precision rate and success
rate when compared with other state-of-the-art tracker
Occlusion-Robust MVO: Multimotion Estimation Through Occlusion Via Motion Closure
Visual motion estimation is an integral and well-studied challenge in
autonomous navigation. Recent work has focused on addressing multimotion
estimation, which is especially challenging in highly dynamic environments.
Such environments not only comprise multiple, complex motions but also tend to
exhibit significant occlusion.
Previous work in object tracking focuses on maintaining the integrity of
object tracks but usually relies on specific appearance-based descriptors or
constrained motion models. These approaches are very effective in specific
applications but do not generalize to the full multimotion estimation problem.
This paper presents a pipeline for estimating multiple motions, including the
camera egomotion, in the presence of occlusions. This approach uses an
expressive motion prior to estimate the SE (3) trajectory of every motion in
the scene, even during temporary occlusions, and identify the reappearance of
motions through motion closure. The performance of this occlusion-robust
multimotion visual odometry (MVO) pipeline is evaluated on real-world data and
the Oxford Multimotion Dataset.Comment: To appear at the 2020 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS). An earlier version of this work first
appeared at the Long-term Human Motion Planning Workshop (ICRA 2019). 8
pages, 5 figures. Video available at
https://www.youtube.com/watch?v=o_N71AA6FR
Recognition of nonmanual markers in American Sign Language (ASL) using non-parametric adaptive 2D-3D face tracking
This paper addresses the problem of automatically recognizing linguistically significant nonmanual expressions in American Sign Language from video. We develop a fully automatic system that is able to track facial expressions and head movements, and detect and recognize facial events continuously from video. The main contributions of the proposed framework are the following: (1) We have built a stochastic and adaptive ensemble of face trackers to address factors resulting in lost face track; (2) We combine 2D and 3D deformable face models to warp input frames, thus correcting for any variation in facial appearance resulting from changes in 3D head pose; (3) We use a combination of geometric features and texture features extracted from a canonical frontal representation. The proposed new framework makes it possible to detect grammatically significant nonmanual expressions from continuous signing and to differentiate successfully among linguistically significant expressions that involve subtle differences in appearance. We present results that are based on the use of a dataset containing 330 sentences from videos that were collected and linguistically annotated at Boston University
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