2 research outputs found
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
Tracking interacting targets in multi-modal sensors
PhDObject tracking is one of the fundamental tasks in various applications such as surveillance,
sports, video conferencing and activity recognition. Factors such as occlusions,
illumination changes and limited field of observance of the sensor make tracking a challenging
task. To overcome these challenges the focus of this thesis is on using multiple
modalities such as audio and video for multi-target, multi-modal tracking. Particularly,
this thesis presents contributions to four related research topics, namely, pre-processing of
input signals to reduce noise, multi-modal tracking, simultaneous detection and tracking,
and interaction recognition.
To improve the performance of detection algorithms, especially in the presence
of noise, this thesis investigate filtering of the input data through spatio-temporal feature
analysis as well as through frequency band analysis. The pre-processed data from multiple
modalities is then fused within Particle filtering (PF). To further minimise the discrepancy
between the real and the estimated positions, we propose a strategy that associates the
hypotheses and the measurements with a real target, using a Weighted Probabilistic Data
Association (WPDA). Since the filtering involved in the detection process reduces the
available information and is inapplicable on low signal-to-noise ratio data, we investigate
simultaneous detection and tracking approaches and propose a multi-target track-beforedetect
Particle filtering (MT-TBD-PF). The proposed MT-TBD-PF algorithm bypasses
the detection step and performs tracking in the raw signal. Finally, we apply the proposed
multi-modal tracking to recognise interactions between targets in regions within, as well
as outside the cameras’ fields of view.
The efficiency of the proposed approaches are demonstrated on large uni-modal,
multi-modal and multi-sensor scenarios from real world detections, tracking and event
recognition datasets and through participation in evaluation campaigns