4,877 research outputs found
Data-Driven Modeling of Group Entitativity in Virtual Environments
We present a data-driven algorithm to model and predict the socio-emotional
impact of groups on observers. Psychological research finds that highly
entitative i.e. cohesive and uniform groups induce threat and unease in
observers. Our algorithm models realistic trajectory-level behaviors to
classify and map the motion-based entitativity of crowds. This mapping is based
on a statistical scheme that dynamically learns pedestrian behavior and
computes the resultant entitativity induced emotion through group motion
characteristics. We also present a novel interactive multi-agent simulation
algorithm to model entitative groups and conduct a VR user study to validate
the socio-emotional predictive power of our algorithm. We further show that
model-generated high-entitativity groups do induce more negative emotions than
low-entitative groups.Comment: Accepted at VRST 2018, November 28-December 1, 2018, Tokyo, Japa
Pedestrian Dominance Modeling for Socially-Aware Robot Navigation
We present a Pedestrian Dominance Model (PDM) to identify the dominance
characteristics of pedestrians for robot navigation. Through a perception study
on a simulated dataset of pedestrians, PDM models the perceived dominance
levels of pedestrians with varying motion behaviors corresponding to
trajectory, speed, and personal space. At runtime, we use PDM to identify the
dominance levels of pedestrians to facilitate socially-aware navigation for the
robots. PDM can predict dominance levels from trajectories with ~85% accuracy.
Prior studies in psychology literature indicate that when interacting with
humans, people are more comfortable around people that exhibit complementary
movement behaviors. Our algorithm leverages this by enabling the robots to
exhibit complementing responses to pedestrian dominance. We also present an
application of PDM for generating dominance-based collision-avoidance behaviors
in the navigation of autonomous vehicles among pedestrians. We demonstrate the
benefits of our algorithm for robots navigating among tens of pedestrians in
simulated environments.Comment: To Appear in ICRA 201
Dynamic Matrix Decomposition for Action Recognition
Designing a technique for the automatic analysis of different actions in
videos in order to detect the presence of interested activities is of high
significance nowadays. In this paper, we explore a robust and dynamic
appearance technique for the purpose of identifying different action
activities. We also exploit a low-rank and structured sparse matrix
decomposition (LSMD) method to better model these activities.. Our method is
effective in encoding localized spatio-temporal features which enables the
analysis of local motion taking place in the video. Our proposed model use
adjacent frame differences as the input to the method thereby forcing it to
capture the changes occurring in the video. The performance of our model is
tested on a benchmark dataset in terms of detection accuracy. Results achieved
with our model showed the promising capability of our model in detecting action
activities
Deep Trajectory for Recognition of Human Behaviours
Identifying human actions in complex scenes is widely considered as a
challenging research problem due to the unpredictable behaviors and variation
of appearances and postures. For extracting variations in motion and postures,
trajectories provide meaningful way. However, simple trajectories are normally
represented by vector of spatial coordinates. In order to identify human
actions, we must exploit structural relationship between different
trajectories. In this paper, we propose a method that divides the video into N
number of segments and then for each segment we extract trajectories. We then
compute trajectory descriptor for each segment which capture the structural
relationship among different trajectories in the video segment. For trajectory
descriptor, we project all extracted trajectories on the canvas. This will
result in texture image which can store the relative motion and structural
relationship among the trajectories. We then train Convolution Neural Network
(CNN) to capture and learn the representation from dense trajectories. .
Experimental results shows that our proposed method out performs state of the
art methods by 90.01% on benchmark data set
Automatic Dataset Augmentation Using Virtual Human Simulation
Virtual Human Simulation has been widely used for different purposes, such as
comfort or accessibility analysis. In this paper, we investigate the
possibility of using this type of technique to extend the training datasets of
pedestrians to be used with machine learning techniques. Our main goal is to
verify if Computer Graphics (CG) images of virtual humans with a simplistic
rendering can be efficient in order to augment datasets used for training
machine learning methods. In fact, from a machine learning point of view, there
is a need to collect and label large datasets for ground truth, which sometimes
demands manual annotation. In addition, find out images and videos with real
people and also provide ground truth of people detection and counting is not
trivial. If CG images, which can have a ground truth automatically generated,
can also be used as training in machine learning techniques for pedestrian
detection and counting, it can certainly facilitate and optimize the whole
process of event detection. In particular, we propose to parametrize virtual
humans using a data-driven approach. Results demonstrated that using the
extended datasets with CG images outperforms the results when compared to only
real images sequences
Predicting Future Pedestrian Motion in Video Sequences using Crowd Simulation
While human and group analysis have become an important area in last decades,
some current and relevant applications involve to estimate future motion of
pedestrians in real video sequences. This paper presents a method to provide
motion estimation of real pedestrians in next seconds, using crowd simulation.
Our method is based on Physics and heuristics and use BioCrowds as crowd
simulation methodology to estimate future positions of people in video
sequences. Results show that our method for estimation works well even for
complex videos where events can happen. The maximum achieved average error is
cm when estimating the future motion of 32 pedestrians with more than 2
seconds in advance. This paper discusses this and other results
Characterizing Human Behaviours Using Statistical Motion Descriptor
Identifying human behaviors is a challenging research problem due to the
complexity and variation of appearances and postures, the variation of camera
settings, and view angles. In this paper, we try to address the problem of
human behavior identification by introducing a novel motion descriptor based on
statistical features. The method first divide the video into N number of
temporal segments. Then for each segment, we compute dense optical flow, which
provides instantaneous velocity information for all the pixels. We then compute
Histogram of Optical Flow (HOOF) weighted by the norm and quantized into 32
bins. We then compute statistical features from the obtained HOOF forming a
descriptor vector of 192- dimensions. We then train a non-linear multi-class
SVM that classify different human behaviors with the accuracy of 72.1%. We
evaluate our method by using publicly available human action data set.
Experimental results shows that our proposed method out performs state of the
art methods
Crowd Behavior Analysis: A Review where Physics meets Biology
Although the traits emerged in a mass gathering are often non-deliberative,
the act of mass impulse may lead to irre- vocable crowd disasters. The two-fold
increase of carnage in crowd since the past two decades has spurred significant
advances in the field of computer vision, towards effective and proactive crowd
surveillance. Computer vision stud- ies related to crowd are observed to
resonate with the understanding of the emergent behavior in physics (complex
systems) and biology (animal swarm). These studies, which are inspired by
biology and physics, share surprisingly common insights, and interesting
contradictions. However, this aspect of discussion has not been fully explored.
Therefore, this survey provides the readers with a review of the
state-of-the-art methods in crowd behavior analysis from the physics and
biologically inspired perspectives. We provide insights and comprehensive
discussions for a broader understanding of the underlying prospect of blending
physics and biology studies in computer vision.Comment: Accepted in Neurocomputing, 31 pages, 180 reference
Towards a Crowd Analytic Framework For Crowd Management in Majid-al-Haram
The scared cities of Makkah Al Mukarramah and Madina Al Munawarah host
millions of pilgrims every year. During Hajj, the movement of large number of
people has a unique spatial and temporal constraints, which makes Hajj one of
toughest challenges for crowd management. In this paper, we propose a computer
vision based framework that automatically analyses video sequence and computes
important measurements which include estimation of crowd density,
identification of dominant patterns, detection and localization of congestion.
In addition, we analyze helpful statistics of the crowd like speed, and
direction, that could provide support to crowd management personnel. The
framework presented in this paper indicate that new advances in computer vision
and machine learning can be leveraged effectively for challenging and high
density crowd management applications. However, significant customization of
existing approaches is required to apply them to the challenging crowd
management situations in Masjid Al Haram. Our results paint a promising picture
for deployment of computer vision technologies to assist in quantitative
measurement of crowd size, density and congestion.Comment: 17th Scientific Meeting on Hajj & Umrah Research, 201
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