51 research outputs found
Convolutional Drift Networks for Video Classification
Analyzing spatio-temporal data like video is a challenging task that requires
processing visual and temporal information effectively. Convolutional Neural
Networks have shown promise as baseline fixed feature extractors through
transfer learning, a technique that helps minimize the training cost on visual
information. Temporal information is often handled using hand-crafted features
or Recurrent Neural Networks, but this can be overly specific or prohibitively
complex. Building a fully trainable system that can efficiently analyze
spatio-temporal data without hand-crafted features or complex training is an
open challenge. We present a new neural network architecture to address this
challenge, the Convolutional Drift Network (CDN). Our CDN architecture combines
the visual feature extraction power of deep Convolutional Neural Networks with
the intrinsically efficient temporal processing provided by Reservoir
Computing. In this introductory paper on the CDN, we provide a very simple
baseline implementation tested on two egocentric (first-person) video activity
datasets.We achieve video-level activity classification results on-par with
state-of-the art methods. Notably, performance on this complex spatio-temporal
task was produced by only training a single feed-forward layer in the CDN.Comment: Published in IEEE Rebooting Computin
Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition
A major emerging challenge is how to protect people's privacy as cameras and
computer vision are increasingly integrated into our daily lives, including in
smart devices inside homes. A potential solution is to capture and record just
the minimum amount of information needed to perform a task of interest. In this
paper, we propose a fully-coupled two-stream spatiotemporal architecture for
reliable human action recognition on extremely low resolution (e.g., 12x16
pixel) videos. We provide an efficient method to extract spatial and temporal
features and to aggregate them into a robust feature representation for an
entire action video sequence. We also consider how to incorporate high
resolution videos during training in order to build better low resolution
action recognition models. We evaluate on two publicly-available datasets,
showing significant improvements over the state-of-the-art.Comment: 9 pagers, 5 figures, published in WACV 201
Drive Video Analysis for the Detection of Traffic Near-Miss Incidents
Because of their recent introduction, self-driving cars and advanced driver
assistance system (ADAS) equipped vehicles have had little opportunity to
learn, the dangerous traffic (including near-miss incident) scenarios that
provide normal drivers with strong motivation to drive safely. Accordingly, as
a means of providing learning depth, this paper presents a novel traffic
database that contains information on a large number of traffic near-miss
incidents that were obtained by mounting driving recorders in more than 100
taxis over the course of a decade. The study makes the following two main
contributions: (i) In order to assist automated systems in detecting near-miss
incidents based on database instances, we created a large-scale traffic
near-miss incident database (NIDB) that consists of video clip of dangerous
events captured by monocular driving recorders. (ii) To illustrate the
applicability of NIDB traffic near-miss incidents, we provide two primary
database-related improvements: parameter fine-tuning using various near-miss
scenes from NIDB, and foreground/background separation into motion
representation. Then, using our new database in conjunction with a monocular
driving recorder, we developed a near-miss recognition method that provides
automated systems with a performance level that is comparable to a human-level
understanding of near-miss incidents (64.5% vs. 68.4% at near-miss recognition,
61.3% vs. 78.7% at near-miss detection).Comment: Accepted to ICRA 201
Learning Robot Activities from First-Person Human Videos Using Convolutional Future Regression
We design a new approach that allows robot learning of new activities from
unlabeled human example videos. Given videos of humans executing the same
activity from a human's viewpoint (i.e., first-person videos), our objective is
to make the robot learn the temporal structure of the activity as its future
regression network, and learn to transfer such model for its own motor
execution. We present a new deep learning model: We extend the state-of-the-art
convolutional object detection network for the representation/estimation of
human hands in training videos, and newly introduce the concept of using a
fully convolutional network to regress (i.e., predict) the intermediate scene
representation corresponding to the future frame (e.g., 1-2 seconds later).
Combining these allows direct prediction of future locations of human hands and
objects, which enables the robot to infer the motor control plan using our
manipulation network. We experimentally confirm that our approach makes
learning of robot activities from unlabeled human interaction videos possible,
and demonstrate that our robot is able to execute the learned collaborative
activities in real-time directly based on its camera input
Multichannel Attention Network for Analyzing Visual Behavior in Public Speaking
Public speaking is an important aspect of human communication and
interaction. The majority of computational work on public speaking concentrates
on analyzing the spoken content, and the verbal behavior of the speakers. While
the success of public speaking largely depends on the content of the talk, and
the verbal behavior, non-verbal (visual) cues, such as gestures and physical
appearance also play a significant role. This paper investigates the importance
of visual cues by estimating their contribution towards predicting the
popularity of a public lecture. For this purpose, we constructed a large
database of more than TED talk videos. As a measure of popularity of the
TED talks, we leverage the corresponding (online) viewers' ratings from
YouTube. Visual cues related to facial and physical appearance, facial
expressions, and pose variations are extracted from the video frames using
convolutional neural network (CNN) models. Thereafter, an attention-based long
short-term memory (LSTM) network is proposed to predict the video popularity
from the sequence of visual features. The proposed network achieves
state-of-the-art prediction accuracy indicating that visual cues alone contain
highly predictive information about the popularity of a talk. Furthermore, our
network learns a human-like attention mechanism, which is particularly useful
for interpretability, i.e. how attention varies with time, and across different
visual cues by indicating their relative importance
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.
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