Multi-class Boosting for Early Classification of Sequences

Consider the problem of driver behavior recognition from images captured by a camera installed in a vehicle [4]. Recognition of driver behavior is crucial for driver assistance systems that make driving comfortable and safe. One notable requirement for real applicatioins is that we would like to predict and classify a behavior as quickly as possible: if we detect a sign of dangerous movements such as mobile phone use while driving, we would like to warn the driver quickly before the behavior causes any accidents. This kind of classification task is called “early classification (recognition),” and is important for many practical problems including on-line handwritten character recognition, and speech recognition systems. In this paper, we focus one of the most famous discriminative models, i.e. Adaboost [1, 2], and extend it for early classification of sequences. While existing researches (e.g. [5, 6]) have studied only a binary classification problem, we present a multi-class extension of Adaboost for early classification, called Earlyboost.MH (Fig. 1). In this paper, we propose an efficient multi-class Adaboost for early classification by combining multi-class Adaboost.MH [3] and the early classification Boosting (Earlyboost [6]),