|dc.description.abstract||This thesis presents an automated framework for activity analysis in multi-camera
setups. We start with the calibration of cameras particularly without overlapping
views. An algorithm is presented that exploits trajectory observations in each view
and works iteratively on camera pairs. First outliers are identified and removed
from observations of each camera. Next, spatio-temporal information derived from
the available trajectory is used to estimate unobserved trajectory segments in areas
uncovered by the cameras. The unobserved trajectory estimates are used to estimate
the relative position of each camera pair, whereas the exit-entrance direction of
each object is used to estimate their relative orientation. The process continues and
iteratively approximates the configuration of all cameras with respect to each other.
Finally, we refi ne the initial configuration estimates with bundle adjustment, based
on the observed and estimated trajectory segments. For cameras with overlapping
views, state-of-the-art homography based approaches are used for calibration.
Next we establish object correspondence across multiple views. Our algorithm
consists of three steps, namely association, fusion and linkage. For association,
local trajectory pairs corresponding to the same physical object are estimated using
multiple spatio-temporal features on a common ground plane. To disambiguate
spurious associations, we employ a hybrid approach that utilises the matching results
on the image plane and ground plane. The trajectory segments after association
are fused by adaptive averaging. Trajectory linkage then integrates segments and generates a single trajectory of an object across the entire observed area.
Finally, for activities analysis clustering is applied on complete trajectories. Our
clustering algorithm is based on four main steps, namely the extraction of a set of
representative trajectory features, non-parametric clustering, cluster merging and
information fusion for the identification of normal and rare object motion patterns.
First we transform the trajectories into a set of feature spaces on which Meanshift
identi es the modes and the corresponding clusters. Furthermore, a merging
procedure is devised to re fine these results by combining similar adjacent clusters.
The fi nal common patterns are estimated by fusing the clustering results across all
feature spaces. Clusters corresponding to reoccurring trajectories are considered as
normal, whereas sparse trajectories are associated to abnormal and rare events.
The performance of the proposed framework is evaluated on standard data-sets
and compared with state-of-the-art techniques. Experimental results show that
the proposed framework outperforms state-of-the-art algorithms both in terms of
accuracy and robustness.||en_US