Multi-target tracking and performance evaluation on videos
Abstract
Multi-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 surveillance
Authors
Poiesi, FabioCollections
- Theses [3834]