Visual Object Tracking: The Initialisation Problem

Model initialisation is an important component of object tracking. Tracking algorithms are generally provided with the first frame of a sequence and a bounding box (BB) indicating the location of the object. This BB may contain a large number of background pixels in addition to the object and can lead to parts-based tracking algorithms initialising their object models in background regions of the BB. In this paper, we tackle this as a missing labels problem, marking pixels sufficiently away from the BB as belonging to the background and learning the labels of the unknown pixels. Three techniques, One-Class SVM (OC-SVM), Sampled-Based Background Model (SBBM) (a novel background model based on pixel samples), and Learning Based Digital Matting (LBDM), are adapted to the problem. These are evaluated with leave-one-video-out cross-validation on the VOT2016 tracking benchmark. Our evaluation shows both OC-SVMs and SBBM are capable of providing a good level of segmentation accuracy but are too parameter-dependent to be used in real-world scenarios. We show that LBDM achieves significantly increased performance with parameters selected by cross validation and we show that it is robust to parameter variation.Comment: 15th Conference on Computer and Robot Vision (CRV 2018). Source code available at https://github.com/georgedeath/initialisation-proble

Time series segmentation with shifting means hidden markov models

International audienceWe present a new family of hidden Markov models and apply these to the segmentation of hydrological and environmental time series. The proposed hidden Markov models have a discrete state space and their structure is inspired from the shifting means models introduced by Chernoff and Zacks and by Salas and Boes. An estimation method inspired from the EM algorithm is proposed, and we show that it can accurately identify multiple change-points in a time series. We also show that the solution obtained using this algorithm can serve as a starting point for a Monte-Carlo Markov chain Bayesian estimation method, thus reducing the computing time needed for the Markov chain to converge to a stationary distribution

Analysis of coupling effect on twin waveguides defined by ion-implanted AlGaAs/GaAs quantum wells

An accurate model is presented for the analysis of ion-implanted AlGaAs/GaAs multi-quantum well symmetric and asymmetric twin waveguides. The modal propagation constants, modal indices and field profiles of the leading supermodes are solved numerically by using a quasi-vector method based on the Finite Difference method. Impurity induced disordering defined multi-quantum well twin waveguides are shown to have similar optical properties as conventional dielectric rib waveguides. They provide a more flexible control over the waveguiding and coupling characteristics by changing the diffusion time, the ion implant energy, the mask width, the waveguide separation, and the operating wavelength. By suitably varying these parameters, single-mode operation can be achieved, while the coupling length can be theoretically tuned from a few millimeters to a hundred meters, a difference in the order of lOs. Impurity induced disordering produced waveguide arrays are therefore highly recommended for integrated photonic IC realisation.published_or_final_versio