Dynamic multi-channel feature dictionaries for robust object tracking

Abstract

Current state-of-the-art trackers use low-resolution image intensity features as part of object appearance modeling. Such features often fail to capture sufficient visual information about the object, and ultimately drift away. In our work, we employ visually richer representation schemes to model the appearance of the object. Specifically, we construct multi-channel feature dictionaries using image intensity, normalized gradient magnitude, and quantized gradient orientation information. To further mitigate the tracking drift problem, we take into account the dynamics of the past state vectors of the object, and propose a novel dynamic adaptive state transition model. We also demonstrate the computational tractability of using richer appearance modeling schemes by adaptively pruning candidate particles during each sampling step, and using a fast augmented Lagrangian technique to solve the associated optimization problem.