Stochastic (or random) processes are inherent to numerous fields of human endeavour including engineering, science, and business and finance. This thesis presents multiple novel methods for quickly detecting and estimating uncertainties in several important classes of stochastic processes. The significance of these novel methods is demonstrated by employing them to detect aircraft manoeuvres in video signals in the important application of autonomous mid-air collision avoidance
Changes in motion properties of trajectories provide useful cues for modeling and recognizing human ...
Rapid recursive estimation of hidden Markov Model (HMM) parameters is important in applications that...
The intention of drivers to start discrete manoeuvres (like a lane change or a turn manoeuvre) is id...
Most models of aircraft trajectories are non-linear and stochastic in nature; and their internal par...
AbstractAs an extension to the popular hidden Markov model (HMM), a hidden semi-Markov model (HSMM) ...
We consider the problem of quickly detecting an unknown change in a sequence of independent random v...
Machine vision is emerging as a viable sensing approach for mid-air collision avoidance (particularl...
We investigate the quickest detection of an unknown change in the distribution of a stochastic proce...
This paper considers the problem of joint change detection and identification assuming multiple comp...
Abstract Ever increasing the robust tracking of abrupt motion is a challenging task in computer visi...
In this paper we present a change detection approach for dependent processes based on the output of ...
Event recognition is probably the ultimate purpose of an automated surveillance system. In this pape...
In this paper, we consider the problem of quickly detecting an unknown change in the conditional den...
This paper investigates compressed sensing using hidden Markov models (HMMs) and hence provides an e...
The maximum likelihood algorithm is introduced for measuring the unknown moment of abrupt change and...
Changes in motion properties of trajectories provide useful cues for modeling and recognizing human ...
Rapid recursive estimation of hidden Markov Model (HMM) parameters is important in applications that...
The intention of drivers to start discrete manoeuvres (like a lane change or a turn manoeuvre) is id...
Most models of aircraft trajectories are non-linear and stochastic in nature; and their internal par...
AbstractAs an extension to the popular hidden Markov model (HMM), a hidden semi-Markov model (HSMM) ...
We consider the problem of quickly detecting an unknown change in a sequence of independent random v...
Machine vision is emerging as a viable sensing approach for mid-air collision avoidance (particularl...
We investigate the quickest detection of an unknown change in the distribution of a stochastic proce...
This paper considers the problem of joint change detection and identification assuming multiple comp...
Abstract Ever increasing the robust tracking of abrupt motion is a challenging task in computer visi...
In this paper we present a change detection approach for dependent processes based on the output of ...
Event recognition is probably the ultimate purpose of an automated surveillance system. In this pape...
In this paper, we consider the problem of quickly detecting an unknown change in the conditional den...
This paper investigates compressed sensing using hidden Markov models (HMMs) and hence provides an e...
The maximum likelihood algorithm is introduced for measuring the unknown moment of abrupt change and...
Changes in motion properties of trajectories provide useful cues for modeling and recognizing human ...
Rapid recursive estimation of hidden Markov Model (HMM) parameters is important in applications that...
The intention of drivers to start discrete manoeuvres (like a lane change or a turn manoeuvre) is id...