Robust visual tracking based on product sparse coding

Visual tracking in high-dimensional particle filter

In this paper, we propose a novel object tracking algorithm by using high-dimensional particle filter and combined features. Firstly, the refined two-dimensional principal component analysis and the tendency are combined to represent an object. Secondly, we present a framework using high-order Monte Carlo Markov Chain which considers more information and performs more discriminative and efficient on moving objects than the traditional first-order particle filtering. Finally, an advanced sequential importance resampling is applied to estimate the posterior density and obtains the high-quality particles. To further gain the better samples, K-means clustering is used to select more typical particles, which reduces the computational cost. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the performance of our proposed algorithm is superior to the state-of-the-art methods.

Optimal Appearance Model for Visual Tracking

Many studies argue that integrating multiple cues in an adaptive way increases tracking performance. However, what is the definition of adaptiveness and how to realize it remains an open issue. On the premise that the model with optimal discriminative ability is also optimal for tracking the target, this work realizes adaptiveness and robustness through the optimization of multi-cue integration models. Specifically, based on prior knowledge and current observation, a set of discrete samples are generated to approximate the foreground and background distribution. With the goal of optimizing the classification margin, an objective function is defined, and the appearance model is optimized by introducing optimization algorithms. The proposed optimized appearance model framework is embedded into a particle filter for a field test, and it is demonstrated to be robust against various kinds of complex tracking conditions. This model is general and can be easily extended to other parameterized multi-cue models.