Automatic pedestrian detection and tracking with a multiple-cue max-margin framework

Object tracking is a computer vision task of predicting objects' locations in a video sequence. Estimating an object's trajectory is usually accomplished by a combination of cues, including an appearance model that describes the appearance of the target object and a motion model that describes object dynamics. In this thesis, we present MMTrack, a principled framework for integrating multiple cues in object tracking. The framework formularizes object tracking as a structured prediction problem solved with Structural Support Vector Machine. The formulation features joint learning of appearance and motion model parameters, as well as incorporation of descriptive and discriminative appearance models. We also show a fully automatic pedestrian detection and tracking system based on MMTrack, and present its performance on real-world data sets.