Advanced methods and extensions for kernel-based object tracking

Abstract

In today's world, the rapid developments in computing technology have generated a great deal of interest in automated video analysis systems such as smart cars, video indexing services and advanced surveillance networks. Amongst those, object tracking research plays a pivotal role in providing an unobtrusive means to detect, track and alert the presence of the objects of interest in a given scene with little to no supervisor interaction. The application fields for object tracking can vary from smart vehicles, advanced surveillance networks to sport analysis and perceptual user interfaces. The diversity in its applications also gives rise to a number of different tracking algorithms tailored to suit the corresponding scenarios and constraints. Along this line, the kernel-based tracker has emerged as one of the benchmark tracking algorithms due to its real-time performance, robustness to noise and tracking accuracy. In this thesis, we explore the possibility of further enhancing the original kernel-based tracker. We do this by firstly developing a probabilistic formulation for the mean-shift algorithm which, in turn, provides a means to estimate the target's severe transformations in size, shape and orientation. For changes in colour appearance due to poor lighting condition of the scene, we opt to combine multiple low complexity image features such as texture, contrast, brightness and colour to improve the tracking performance. To achieve this, we advocate the use of a graphical model to abstract the image features under study into a relational structure and, subsequently, make use of graph-spectral methods to combine them linearly and in a straight-forward manner. Furthermore, we also present an on-line updating method to adjust the tracking model to incorporate new changes in the image features during the course of tracking. To cope with the problem of object occlusion in a high density traffic area, we propose a geometric method to extend the mean-shift algorithm to a 3D setting by the use of multiple cameras with overlapped views of the scene. The methods presented in this thesis not only show significant performance improvements on real-world sequences over a number of benchmark algorithms, but also encompass high generalisation in the spatial and feature domains for future development purposes. Show more