Measuring and reducing observational latency when recognizing actions

An important aspect in interactive, action-based interfaces is the latency in recognizing the action. High latency will cause the system's feedback to lag behind user actions, reducing the overall quality of the user experience. This paper presents a novel dataset and algorithms for reducing the latency in recognizing the action. Latency in classification is minimized with a classifier based on logistic regression that uses canonical poses to identify the action. The classifier is trained from the dataset using a learning formulation that makes it possible to train the classifier to reduce latency. The classifier is compared against both a Bag of Words and a Conditional Random Field classifier and is found to be superior in both pre-segmented and on-line classification tasks.