Embedding motion in model-based stochastic tracking

Particle filtering (PF) is now established as one of the most popular methods for visual tracking. Within this framework, two assumptions are generally made. The first is that the data are temporally independent given the sequence of object states, and the second one is the use of the transition prior as proposal distribution. In this paper, we argue that the first assumption does not strictly hold and that the second can be improved. We propose to handle both modeling issues using motion. Explicit motion measurements are used to drive the sampling process towards the new interesting regions of the image, while implicit motion measurements are introduced in the likelihood evaluation to model the data correlation term. The proposed model allows to handle abrupt motion changes and to filter out visual distractors when tracking objects with generic models based on shape representations. Experimental results compared against the CONDENSATION algorithm have demonstrated superior tracking performance

Embedding Motion in Model-Based Stochastic Tracking

Particle filtering is now established as one of the most popular method for visual tracking. Within this framework, two assumptions are generally made. The first is that the data are temporally independent given the sequence of object states. In this paper, we argue that in general the data are correlated, and that modeling such dependency should improve tracking robustness. The second assumption consists of the use of the transition prior as proposal distribution. Thus, the current observation data is not taken into account, requesting the noise process of this prior to be large enough to handle abrupt trajectory changes. Therefore, many particles are either wasted in low likelihood area, resulting in a low efficiency of the sampling, or, more importantly, propagated on near distractor regions of the image, resulting in tracking failures. In this paper, we propose to handle both issues using motion. Explicit motion measurements are used to drive the sampling process towards the new interesting regions of the image, while implicit motion measurements are introduced in the likelihood evaluation to model the data correlation term. The proposed model allows to handle abrupt motion changes and to filter out visual distractors when tracking objects with generic models based on shape or color distribution representations. Experimental results compared against the CONDENSATION algorithm have demonstrated superior tracking performance

Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems

We investigate a generalised version of the recently proposed ordinal partition time series to network transformation algorithm. Firstly we introduce a fixed time lag for the elements of each partition that is selected using techniques from traditional time delay embedding. The resulting partitions define regions in the embedding phase space that are mapped to nodes in the network space. Edges are allocated between nodes based on temporal succession thus creating a Markov chain representation of the time series. We then apply this new transformation algorithm to time series generated by the R\"ossler system and find that periodic dynamics translate to ring structures whereas chaotic time series translate to band or tube-like structures -- thereby indicating that our algorithm generates networks whose structure is sensitive to system dynamics. Furthermore we demonstrate that simple network measures including the mean out degree and variance of out degrees can track changes in the dynamical behaviour in a manner comparable to the largest Lyapunov exponent. We also apply the same analysis to experimental time series generated by a diode resonator circuit and show that the network size, mean shortest path length and network diameter are highly sensitive to the interior crisis captured in this particular data set