6 research outputs found
The Visual Social Distancing Problem
One of the main and most effective measures to contain the recent viral
outbreak is the maintenance of the so-called Social Distancing (SD). To comply
with this constraint, workplaces, public institutions, transports and schools
will likely adopt restrictions over the minimum inter-personal distance between
people. Given this actual scenario, it is crucial to massively measure the
compliance to such physical constraint in our life, in order to figure out the
reasons of the possible breaks of such distance limitations, and understand if
this implies a possible threat given the scene context. All of this, complying
with privacy policies and making the measurement acceptable. To this end, we
introduce the Visual Social Distancing (VSD) problem, defined as the automatic
estimation of the inter-personal distance from an image, and the
characterization of the related people aggregations. VSD is pivotal for a
non-invasive analysis to whether people comply with the SD restriction, and to
provide statistics about the level of safety of specific areas whenever this
constraint is violated. We then discuss how VSD relates with previous
literature in Social Signal Processing and indicate which existing Computer
Vision methods can be used to manage such problem. We conclude with future
challenges related to the effectiveness of VSD systems, ethical implications
and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this
manuscript and they are listed by alphabetical order. Under submissio
Motion prediction and interaction localisation of people in crowds
PhDThe ability to analyse and predict the movement of people in crowded scenarios can be of
fundamental importance for tracking across multiple cameras and interaction localisation. In this
thesis, we propose a person re-identification method that takes into account the spatial location
of cameras using a plan of the locale and the potential paths people can follow in the unobserved
areas. These potential paths are generated using two models. In the first, people’s trajectories are
constrained to pass through a set of areas of interest (landmarks) in the site. In the second we
integrate a goal-driven approach to the Social Force Model (SFM), initially introduced for crowd
simulation. SFM models the desire of people to reach specific interest points (goals) in a site,
such as exits, shops, seats and meeting points while avoiding walls and barriers. Trajectory propagation
creates the possible re-identification candidates, on which association of people across
cameras is performed using spatial location of the candidates and appearance features extracted
around a person’s head. We validate the proposed method in a challenging scenario from London
Gatwick airport and compare it to state-of-the-art person re-identification methods.
Moreover, we perform detection and tracking of interacting people in a framework based
on SFM that analyses people’s trajectories. The method embeds plausible human behaviours
to predict interactions in a crowd by iteratively minimising the error between predictions and
measurements. We model people approaching a group and restrict the group formation based
on the relative velocity of candidate group members. The detected groups are then tracked by
linking their centres of interaction over time using a buffered graph-based tracker. We show how
the proposed framework outperforms existing group localisation techniques on three publicly
available datasets
Analysing Crowd Behaviours using Mobile Sensing
PhDResearchers have examined crowd behaviour in the past by employing a variety of methods
including ethnographic studies, computer vision techniques and manual annotation-based
data analysis. However, because of the resources to collect, process and analyse data, it
remains difficult to obtain large data sets for study. Mobile phones offer easier means for
data collection that is easy to analyse and can preserve the user’s privacy. The aim of this
thesis is to identify and model different qualities of social interactions inside crowds using
mobile sensing technology. This Ph.D. research makes three main contributions centred
around the mobile sensing and crowd sensing area.
Firstly, an open-source licensed mobile sensing framework is developed, named SensingKit,
that is capable of collecting mobile sensor data from iOS and Android devices,
supporting most sensors available in modern smartphones. The framework has been evaluated
in a case study that investigates the pedestrian gait synchronisation phenomenon.
Secondly, a novel algorithm based on graph theory is proposed capable of detecting
stationary social interactions within crowds. It uses sensor data available in a modern
smartphone device, such as the Bluetooth Smart (BLE) sensor, as an indication of user
proximity, and accelerometer sensor, as an indication of each user’s motion state.
Finally, a machine learning model is introduced that uses multi-modal mobile sensor
data extracted from Bluetooth Smart, accelerometer and gyroscope sensors. The validation
was performed using a relatively large dataset with 24 participants, where they
were asked to socialise with each other for 45 minutes. By using supervised machine
learning based on gradient-boosted trees, a performance increase of 26.7% was achieved
over a proximity-based approach. Such model can be beneficial to the design and implementation
of in-the-wild crowd behavioural analysis, design of influence strategies, and
algorithms for crowd reconfiguration.UK Defence Science & Technology Laboratory (DSTL
Multi-target tracking and performance evaluation on videos
PhDMulti-target tracking is the process that allows the extraction of object motion patterns of
interest from a scene. Motion patterns are often described through metadata representing object
locations and shape information. In the first part of this thesis we discuss the state-of-the-art
methods aimed at accomplishing this task on monocular views and also analyse the methods for
evaluating their performance. The second part of the thesis describes our research contribution
to these topics.
We begin presenting a method for multi-target tracking based on track-before-detect (MTTBD)
formulated as a particle filter. The novelty involves the inclusion of the target identity
(ID) into the particle state, which enables the algorithm to deal with an unknown and unlimited
number of targets. We propose a probabilistic model of particle birth and death based on Markov
Random Fields. This model allows us to overcome the problem of the mixing of IDs of close
targets.
We then propose three evaluation measures that take into account target-size variations, combine
accuracy and cardinality errors, quantify long-term tracking accuracy at different accuracy
levels, and evaluate ID changes relative to the duration of the track in which they occur. This
set of measures does not require pre-setting of parameters and allows one to holistically evaluate
tracking performance in an application-independent manner.
Lastly, we present a framework for multi-target localisation applied on scenes with a high
density of compact objects. Candidate target locations are initially generated by extracting object
features from intensity maps using an iterative method based on a gradient-climbing technique
and an isocontour slicing approach. A graph-based data association method for multi-target
tracking is then applied to link valid candidate target locations over time and to discard those
which are spurious. This method can deal with point targets having indistinguishable appearance
and unpredictable motion.
MT-TBD is evaluated and compared with state-of-the-art methods on real-world surveillanceThis work was supported by the EU, under the FP7 project APIDIS (ICT-216023) and the
Artemis JU and TSB as part of the COPCAMS project (332913)
Detection and tracking of groups in crowd
We propose a method to detect and track interacting people by employing a framework based on a Social Force Model (SFM). The method embeds plausible human behaviors to predict interactions in a crowd by iteratively minimizing the error between predictions and measurements. We model people approaching a group and restrict the group formation based on the relative velocity of candidate group members. The detected groups are then tracked by linking their interaction centers over time using a buffered graph-based tracker. We show how the proposed framework outperforms existing group localization techniques on three publicly available datasets, with improvements of up to 13% on group detection. 1