A naïve Bayes baseline for early gesture recognition.

We approach the problem of early recognizing gestures in video.A naïve Bayes classifier is used to approach the problem.Experiments are conducted in a variety of data sets.Results obtained with the proposed method outperform state of the at. Early gesture/action recognition is the task of determining the identity of a gesture/action with as few information as possible. Although the topic is relatively new, there are some methods that address this problem. However, existing methods rely on complex modeling procedures, that do not necessarily paid off the computational effort. Thus, simple yet effective and efficient techniques are required for this task. This paper describes a new methodology for early gesture recognition based on the well known naïve Bayes classifier. The method is extremely simple and very fast, yet it compares favorably with more elaborated state of the art methodologies. The naïve baseline is based on three main observations: (1) the effectiveness of the naïve Bayes classifier in text mining problems; (2) the link between natural language processing and computer vision via the bag-of-words representation; and (3) the cumulative-evidence nature of the inference process of naïve Bayes. We evaluated the proposed method in several collections that included segmented and continuous video. Experimental results show that the proposed methodology compares favorably with state of the art methodologies that are more elaborated or were specifically designed for this purpose.