1,842 research outputs found
Going Deeper into First-Person Activity Recognition
We bring together ideas from recent work on feature design for egocentric
action recognition under one framework by exploring the use of deep
convolutional neural networks (CNN). Recent work has shown that features such
as hand appearance, object attributes, local hand motion and camera ego-motion
are important for characterizing first-person actions. To integrate these ideas
under one framework, we propose a twin stream network architecture, where one
stream analyzes appearance information and the other stream analyzes motion
information. Our appearance stream encodes prior knowledge of the egocentric
paradigm by explicitly training the network to segment hands and localize
objects. By visualizing certain neuron activation of our network, we show that
our proposed architecture naturally learns features that capture object
attributes and hand-object configurations. Our extensive experiments on
benchmark egocentric action datasets show that our deep architecture enables
recognition rates that significantly outperform state-of-the-art techniques --
an average increase in accuracy over all datasets. Furthermore, by
learning to recognize objects, actions and activities jointly, the performance
of individual recognition tasks also increase by (actions) and
(objects). We also include the results of extensive ablative analysis to
highlight the importance of network design decisions.
Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video
Manual annotations of temporal bounds for object interactions (i.e. start and
end times) are typical training input to recognition, localization and
detection algorithms. For three publicly available egocentric datasets, we
uncover inconsistencies in ground truth temporal bounds within and across
annotators and datasets. We systematically assess the robustness of
state-of-the-art approaches to changes in labeled temporal bounds, for object
interaction recognition. As boundaries are trespassed, a drop of up to 10% is
observed for both Improved Dense Trajectories and Two-Stream Convolutional
Neural Network.
We demonstrate that such disagreement stems from a limited understanding of
the distinct phases of an action, and propose annotating based on the Rubicon
Boundaries, inspired by a similarly named cognitive model, for consistent
temporal bounds of object interactions. Evaluated on a public dataset, we
report a 4% increase in overall accuracy, and an increase in accuracy for 55%
of classes when Rubicon Boundaries are used for temporal annotations.Comment: ICCV 201
Learning to Localize and Align Fine-Grained Actions to Sparse Instructions
Automatic generation of textual video descriptions that are time-aligned with
video content is a long-standing goal in computer vision. The task is
challenging due to the difficulty of bridging the semantic gap between the
visual and natural language domains. This paper addresses the task of
automatically generating an alignment between a set of instructions and a first
person video demonstrating an activity. The sparse descriptions and ambiguity
of written instructions create significant alignment challenges. The key to our
approach is the use of egocentric cues to generate a concise set of action
proposals, which are then matched to recipe steps using object recognition and
computational linguistic techniques. We obtain promising results on both the
Extended GTEA Gaze+ dataset and the Bristol Egocentric Object Interactions
Dataset
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
Predicting the future location of vehicles is essential for safety-critical
applications such as advanced driver assistance systems (ADAS) and autonomous
driving. This paper introduces a novel approach to simultaneously predict both
the location and scale of target vehicles in the first-person (egocentric) view
of an ego-vehicle. We present a multi-stream recurrent neural network (RNN)
encoder-decoder model that separately captures both object location and scale
and pixel-level observations for future vehicle localization. We show that
incorporating dense optical flow improves prediction results significantly
since it captures information about motion as well as appearance change. We
also find that explicitly modeling future motion of the ego-vehicle improves
the prediction accuracy, which could be especially beneficial in intelligent
and automated vehicles that have motion planning capability. To evaluate the
performance of our approach, we present a new dataset of first-person videos
collected from a variety of scenarios at road intersections, which are
particularly challenging moments for prediction because vehicle trajectories
are diverse and dynamic.Comment: To appear on ICRA 201
Analysis of Hand Segmentation in the Wild
A large number of works in egocentric vision have concentrated on action and
object recognition. Detection and segmentation of hands in first-person videos,
however, has less been explored. For many applications in this domain, it is
necessary to accurately segment not only hands of the camera wearer but also
the hands of others with whom he is interacting. Here, we take an in-depth look
at the hand segmentation problem. In the quest for robust hand segmentation
methods, we evaluated the performance of the state of the art semantic
segmentation methods, off the shelf and fine-tuned, on existing datasets. We
fine-tune RefineNet, a leading semantic segmentation method, for hand
segmentation and find that it does much better than the best contenders.
Existing hand segmentation datasets are collected in the laboratory settings.
To overcome this limitation, we contribute by collecting two new datasets: a)
EgoYouTubeHands including egocentric videos containing hands in the wild, and
b) HandOverFace to analyze the performance of our models in presence of similar
appearance occlusions. We further explore whether conditional random fields can
help refine generated hand segmentations. To demonstrate the benefit of
accurate hand maps, we train a CNN for hand-based activity recognition and
achieve higher accuracy when a CNN was trained using hand maps produced by the
fine-tuned RefineNet. Finally, we annotate a subset of the EgoHands dataset for
fine-grained action recognition and show that an accuracy of 58.6% can be
achieved by just looking at a single hand pose which is much better than the
chance level (12.5%).Comment: Accepted at CVPR 201
- …