Multi-target Tracking Using on-line Viterbi Optimisation and Stochastic Modelling
Författare
Summary, in English
To study and compare the safety of intersection, traffic scientists today typically manually monitor the intersection
during several days and count how often certain events such as evasive manoeuvres occur. This is a
laboursome and costly procedure. The aim of this thesis is to provide tools that can reduce the amount of manual
labour required by using automated video analytics. Two methods for creating for such tools are presented.
The first method is a probabilistic background foreground segmentation that for each block of pixels calculate the
probability that this block currently views the static background or some moving foreground object. This is done
by deriving the probability distribution of the normalised cross correlation in the background and the foreground
case respectively. The background distribution depends on the amount of structure in the block.
The second method is a multi-target tracker that uses the probabilistic background foreground segmentation to
produce the trajectories of all objects in the scene. It operates online but with a few seconds delay in order to
incorporate information from both past and future frames when deciding on the current state. This means that the
output is guaranteed to be consistent, i.e. no jumping between different hypothesis, and the respect constrains
placed on the system such as "objects may not occupy the same space at the same time" or "objects may only
appear at the border of the image".
The methods have been tested both on synthetic and numerous sets of real data by implementing applications such
as people counting, loitering detection and traffic surveillance. The applications have been shown to perform very
well as long as the scene studied is not too large.
during several days and count how often certain events such as evasive manoeuvres occur. This is a
laboursome and costly procedure. The aim of this thesis is to provide tools that can reduce the amount of manual
labour required by using automated video analytics. Two methods for creating for such tools are presented.
The first method is a probabilistic background foreground segmentation that for each block of pixels calculate the
probability that this block currently views the static background or some moving foreground object. This is done
by deriving the probability distribution of the normalised cross correlation in the background and the foreground
case respectively. The background distribution depends on the amount of structure in the block.
The second method is a multi-target tracker that uses the probabilistic background foreground segmentation to
produce the trajectories of all objects in the scene. It operates online but with a few seconds delay in order to
incorporate information from both past and future frames when deciding on the current state. This means that the
output is guaranteed to be consistent, i.e. no jumping between different hypothesis, and the respect constrains
placed on the system such as "objects may not occupy the same space at the same time" or "objects may only
appear at the border of the image".
The methods have been tested both on synthetic and numerous sets of real data by implementing applications such
as people counting, loitering detection and traffic surveillance. The applications have been shown to perform very
well as long as the scene studied is not too large.
Avdelning/ar
- Matematik LTH
- Mathematical Imaging Group
Publiceringsår
2009
Språk
Engelska
Fulltext
Dokumenttyp
Doktorsavhandling
Förlag
Centre for Mathematical Sciences, Lund University
Ämne
- Computer Vision and Robotics (Autonomous Systems)
- Mathematics
Status
Published
Forskningsgrupp
- Mathematical Imaging Group
Handledare
ISBN/ISSN/Övrigt
- ISBN: 978-628-7685-2
Försvarsdatum
13 februari 2009
Försvarstid
13:15
Försvarsplats
Lecture Hall MH:C, Centre for Mathematical Sciences, Sölvegatan 18, Lund University, Faculty of Engineering
Opponent
- James Ferryman (Dr)