5,872 research outputs found
Oriented Response Networks
Deep Convolution Neural Networks (DCNNs) are capable of learning
unprecedentedly effective image representations. However, their ability in
handling significant local and global image rotations remains limited. In this
paper, we propose Active Rotating Filters (ARFs) that actively rotate during
convolution and produce feature maps with location and orientation explicitly
encoded. An ARF acts as a virtual filter bank containing the filter itself and
its multiple unmaterialised rotated versions. During back-propagation, an ARF
is collectively updated using errors from all its rotated versions. DCNNs using
ARFs, referred to as Oriented Response Networks (ORNs), can produce
within-class rotation-invariant deep features while maintaining inter-class
discrimination for classification tasks. The oriented response produced by ORNs
can also be used for image and object orientation estimation tasks. Over
multiple state-of-the-art DCNN architectures, such as VGG, ResNet, and STN, we
consistently observe that replacing regular filters with the proposed ARFs
leads to significant reduction in the number of network parameters and
improvement in classification performance. We report the best results on
several commonly used benchmarks.Comment: Accepted in CVPR 2017. Source code available at http://yzhou.work/OR
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
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Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set
Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore, associating tracks in different camera views directly based on their appearance similarity is difficult and prone to error. In most previous methods, the appearance similarity is computed either using color histograms or based on pretrained brightness transfer function that maps color between cameras. In this paper, a novel reference set based appearance model is proposed to improve multitarget tracking in a network of nonoverlapping cameras. Contrary to previous work, a reference set is constructed for a pair of cameras, containing subjects appearing in both camera views. For track association, instead of directly comparing the appearance of two targets in different camera views, they are compared indirectly via the reference set. Besides global color histograms, texture and shape features are extracted at different locations of a target, and AdaBoost is used to learn the discriminative power of each feature. The effectiveness of the proposed method over the state of the art on two challenging real-world multicamera video data sets is demonstrated by thorough experiments
Hierarchical Attention Network for Action Segmentation
The temporal segmentation of events is an essential task and a precursor for
the automatic recognition of human actions in the video. Several attempts have
been made to capture frame-level salient aspects through attention but they
lack the capacity to effectively map the temporal relationships in between the
frames as they only capture a limited span of temporal dependencies. To this
end we propose a complete end-to-end supervised learning approach that can
better learn relationships between actions over time, thus improving the
overall segmentation performance. The proposed hierarchical recurrent attention
framework analyses the input video at multiple temporal scales, to form
embeddings at frame level and segment level, and perform fine-grained action
segmentation. This generates a simple, lightweight, yet extremely effective
architecture for segmenting continuous video streams and has multiple
application domains. We evaluate our system on multiple challenging public
benchmark datasets, including MERL Shopping, 50 salads, and Georgia Tech
Egocentric datasets, and achieves state-of-the-art performance. The evaluated
datasets encompass numerous video capture settings which are inclusive of
static overhead camera views and dynamic, ego-centric head-mounted camera
views, demonstrating the direct applicability of the proposed framework in a
variety of settings.Comment: Published in Pattern Recognition Letter
Egocentric Hand Detection Via Dynamic Region Growing
Egocentric videos, which mainly record the activities carried out by the
users of the wearable cameras, have drawn much research attentions in recent
years. Due to its lengthy content, a large number of ego-related applications
have been developed to abstract the captured videos. As the users are
accustomed to interacting with the target objects using their own hands while
their hands usually appear within their visual fields during the interaction,
an egocentric hand detection step is involved in tasks like gesture
recognition, action recognition and social interaction understanding. In this
work, we propose a dynamic region growing approach for hand region detection in
egocentric videos, by jointly considering hand-related motion and egocentric
cues. We first determine seed regions that most likely belong to the hand, by
analyzing the motion patterns across successive frames. The hand regions can
then be located by extending from the seed regions, according to the scores
computed for the adjacent superpixels. These scores are derived from four
egocentric cues: contrast, location, position consistency and appearance
continuity. We discuss how to apply the proposed method in real-life scenarios,
where multiple hands irregularly appear and disappear from the videos.
Experimental results on public datasets show that the proposed method achieves
superior performance compared with the state-of-the-art methods, especially in
complicated scenarios
RGB-D datasets using microsoft kinect or similar sensors: a survey
RGB-D data has turned out to be a very useful representation of an indoor scene for solving fundamental computer vision problems. It takes the advantages of the color image that provides appearance information of an object and also the depth image that is immune to the variations in color, illumination, rotation angle and scale. With the invention of the low-cost Microsoft Kinect sensor, which was initially used for gaming and later became a popular device for computer vision, high quality RGB-D data can be acquired easily. In recent years, more and more RGB-D image/video datasets dedicated to various applications have become available, which are of great importance to benchmark the state-of-the-art. In this paper, we systematically survey popular RGB-D datasets for different applications including object recognition, scene classification, hand gesture recognition, 3D-simultaneous localization and mapping, and pose estimation. We provide the insights into the characteristics of each important dataset, and compare the popularity and the difficulty of those datasets. Overall, the main goal of this survey is to give a comprehensive description about the available RGB-D datasets and thus to guide researchers in the selection of suitable datasets for evaluating their algorithms
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