2,185 research outputs found
Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras
Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.<br/
Real-World Repetition Estimation by Div, Grad and Curl
We consider the problem of estimating repetition in video, such as performing
push-ups, cutting a melon or playing violin. Existing work shows good results
under the assumption of static and stationary periodicity. As realistic video
is rarely perfectly static and stationary, the often preferred Fourier-based
measurements is inapt. Instead, we adopt the wavelet transform to better handle
non-static and non-stationary video dynamics. From the flow field and its
differentials, we derive three fundamental motion types and three motion
continuities of intrinsic periodicity in 3D. On top of this, the 2D perception
of 3D periodicity considers two extreme viewpoints. What follows are 18
fundamental cases of recurrent perception in 2D. In practice, to deal with the
variety of repetitive appearance, our theory implies measuring time-varying
flow and its differentials (gradient, divergence and curl) over segmented
foreground motion. For experiments, we introduce the new QUVA Repetition
dataset, reflecting reality by including non-static and non-stationary videos.
On the task of counting repetitions in video, we obtain favorable results
compared to a deep learning alternative
Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California
Each year, millions of motor vehicle traffic accidents all over the world
cause a large number of fatalities, injuries and significant material loss.
Automated Driving (AD) has potential to drastically reduce such accidents. In
this work, we focus on the technical challenges that arise from AD in urban
environments. We present the overall architecture of an AD system and describe
in detail the perception and planning modules. The AD system, built on a
modified Acura RLX, was demonstrated in a course in GoMentum Station in
California. We demonstrated autonomous handling of 4 scenarios: traffic lights,
cross-traffic at intersections, construction zones and pedestrians. The AD
vehicle displayed safe behavior and performed consistently in repeated
demonstrations with slight variations in conditions. Overall, we completed 44
runs, encompassing 110km of automated driving with only 3 cases where the
driver intervened the control of the vehicle, mostly due to error in GPS
positioning. Our demonstration showed that robust and consistent behavior in
urban scenarios is possible, yet more investigation is necessary for full scale
roll-out on public roads.Comment: Accepted to Intelligent Vehicles Conference (IV 2017
Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition
The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future
Continuous measurements of real-life bidirectional pedestrian flows on a wide walkway
Employing partially overlapping overhead \kinectTMS sensors and automatic
pedestrian tracking algorithms we recorded the crowd traffic in a rectilinear
section of the main walkway of Eindhoven train station on a 24/7 basis. Beside
giving access to the train platforms (it passes underneath the railways), the
walkway plays an important connection role in the city. Several crowding
scenarios occur during the day, including high- and low-density dynamics in
uni- and bi-directional regimes. In this paper we discuss our recording
technique and we illustrate preliminary data analyses. Via fundamental
diagrams-like representations we report pedestrian velocities and fluxes vs.
pedestrian density. Considering the density range - ped/m, we
find that at densities lower than ped/m pedestrians in
unidirectional flows walk faster than in bidirectional regimes. On the
opposite, velocities and fluxes for even bidirectional flows are higher above
ped/m.Comment: 9 pages, 7 figure
Pedestrian, Crowd, and Evacuation Dynamics
This contribution describes efforts to model the behavior of individual
pedestrians and their interactions in crowds, which generate certain kinds of
self-organized patterns of motion. Moreover, this article focusses on the
dynamics of crowds in panic or evacuation situations, methods to optimize
building designs for egress, and factors potentially causing the breakdown of
orderly motion.Comment: This is a review paper. For related work see http://www.soms.ethz.c
Articulated human tracking and behavioural analysis in video sequences
Recently, there has been a dramatic growth of interest in the observation and tracking
of human subjects through video sequences. Arguably, the principal impetus has come
from the perceived demand for technological surveillance, however applications in entertainment,
intelligent domiciles and medicine are also increasing. This thesis examines
human articulated tracking and the classi cation of human movement, rst separately
and then as a sequential process.
First, this thesis considers the development and training of a 3D model of human body
structure and dynamics. To process video sequences, an observation model is also designed
with a multi-component likelihood based on edge, silhouette and colour. This is de ned on
the articulated limbs, and visible from a single or multiple cameras, each of which may be
calibrated from that sequence. Second, for behavioural analysis, we develop a methodology
in which actions and activities are described by semantic labels generated from a Movement
Cluster Model (MCM). Third, a Hierarchical Partitioned Particle Filter (HPPF) was
developed for human tracking that allows multi-level parameter search consistent with the
body structure. This tracker relies on the articulated motion prediction provided by the
MCM at pose or limb level. Fourth, tracking and movement analysis are integrated to
generate a probabilistic activity description with action labels.
The implemented algorithms for tracking and behavioural analysis are tested extensively
and independently against ground truth on human tracking and surveillance
datasets. Dynamic models are shown to predict and generate synthetic motion, while
MCM recovers both periodic and non-periodic activities, de ned either on the whole body
or at the limb level. Tracking results are comparable with the state of the art, however
the integrated behaviour analysis adds to the value of the approach.Overseas Research Students Awards Scheme (ORSAS
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