525 research outputs found
Robust and Efficient Inference of Scene and Object Motion in Multi-Camera Systems
Multi-camera systems have the ability to overcome some of the fundamental limitations of single camera based systems. Having multiple view points of a scene goes a long way in limiting the influence of field of view, occlusion, blur and poor resolution of an individual camera. This dissertation addresses robust and efficient inference of object motion and scene in multi-camera and multi-sensor systems.
The first part of the dissertation discusses the role of constraints introduced by projective imaging towards robust inference of multi-camera/sensor based object motion. We discuss the role of the homography and epipolar constraints for fusing object motion perceived by individual cameras. For planar scenes, the homography constraints provide a natural mechanism for data association. For scenes that are not planar, the epipolar constraint provides a weaker multi-view relationship. We use the epipolar constraint for tracking in multi-camera and multi-sensor networks. In particular, we show that the epipolar constraint reduces the dimensionality of the state space of the
problem by introducing a ``shared'' state space for the joint tracking problem. This allows for robust tracking even when one of the sensors fail due to poor SNR or occlusion.
The second part of the dissertation deals with challenges in the computational aspects of tracking algorithms that are common to such systems. Much of the inference in the multi-camera and multi-sensor networks deal with complex non-linear models corrupted with non-Gaussian noise. Particle filters provide approximate Bayesian inference in such settings. We analyze the computational drawbacks of traditional particle filtering algorithms, and present a method for implementing the particle filter using the Independent Metropolis Hastings sampler, that is highly amenable to pipelined implementations and parallelization. We analyze the implementations of the proposed algorithm, and in particular concentrate on implementations that have
minimum processing times.
The last part of the dissertation deals with the efficient sensing paradigm of compressing sensing (CS) applied to signals in imaging, such as natural images and reflectance fields. We propose a hybrid signal model on the assumption that most real-world signals exhibit subspace compressibility as well as sparse representations. We show that several real-world visual signals such as images, reflectance fields, videos etc., are better approximated by this hybrid of two models. We derive optimal hybrid linear projections of the signal and show that theoretical guarantees and algorithms designed for CS can be easily extended to hybrid subspace-compressive sensing. Such methods reduce the
amount of information sensed by a camera, and help in reducing the so called data deluge problem in large multi-camera systems
Enhanced particle PHD filtering for multiple human tracking
PhD ThesisVideo-based single human tracking has found wide application but multiple
human tracking is more challenging and enhanced processing techniques are
required to estimate the positions and number of targets in each frame. In
this thesis, the particle probability hypothesis density (PHD) lter is therefore
the focus due to its ability to estimate both localization and cardinality
information related to multiple human targets. To improve the tracking performance
of the particle PHD lter, a number of enhancements are proposed.
The Student's-t distribution is employed within the state and measurement
models of the PHD lter to replace the Gaussian distribution because
of its heavier tails, and thereby better predict particles with larger amplitudes.
Moreover, the variational Bayesian approach is utilized to estimate
the relationship between the measurement noise covariance matrix and the
state model, and a joint multi-dimensioned Student's-t distribution is exploited.
In order to obtain more observable measurements, a backward retrodiction
step is employed to increase the measurement set, building upon the
concept of a smoothing algorithm. To make further improvement, an adaptive
step is used to combine the forward ltering and backward retrodiction
ltering operations through the similarities of measurements achieved over
discrete time. As such, the errors in the delayed measurements generated by
false alarms and environment noise are avoided.
In the nal work, information describing human behaviour is employed
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Abstract v
to aid particle sampling in the prediction step of the particle PHD lter,
which is captured in a social force model. A novel social force model is
proposed based on the exponential function. Furthermore, a Markov Chain
Monte Carlo (MCMC) step is utilized to resample the predicted particles,
and the acceptance ratio is calculated by the results from the social force
model to achieve more robust prediction. Then, a one class support vector
machine (OCSVM) is applied in the measurement model of the PHD lter,
trained on human features, to mitigate noise from the environment and to
achieve better tracking performance.
The proposed improvements of the particle PHD lters are evaluated
with benchmark datasets such as the CAVIAR, PETS2009 and TUD datasets
and assessed with quantitative and global evaluation measures, and are compared
with state-of-the-art techniques to con rm the improvement of multiple
human tracking performance
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