We present a framework for supervised subspace tracking, when there are two time series xt and yt, one being the high-dimensional predictors and the other being the response variables and the subspace tracking needs to take into consideration of both sequences. It extends the classic online subspace tracking work which can be viewed as tracking of xt only. Our online sufficient dimensionality r...

We present a new appearance model for online multi-target tracking, where a patch-based representation of each target to be tracked is introduced along with a sequential update scheme, which we call “motion agreement tracking” (MAT). This method updates the appearance model of each target online by indirectly evaluating the motion consistency among its local patches. We show that a distance mea...

Tracking by sequential Bayesian filtering relies on a graphical model with temporally ordered linear structure based on temporal smoothness assumption. This framework is convenient to propagate the posterior through the first-order Markov chain. However, density propagation from a single immediately preceding frame may be unreliable especially in challenging situations such as abrupt appearance...

We present the results of an eye-tracking study on a Massive Open Online Course (MOOC) lecture showing the relation between gaze variables and students’ performance and learning strategy. 40 students watched a MOOC lecture while their eye-movements were being recorded. We present a method to define stimuli-based gaze variables that can be defined for any kind of stimulus. The advantage of using...

We present an eye-tracking study to show the different gaze patterns across the MOOC learners. We hypothesize that the pretests can be used to shape the attention of the students. Moreover we also proposed a collaborative add on activity to the learning material for the students to help them reflect on the material they learnt in the MOOC video. What comes out of the present study is two differ...