Mining discriminative descriptors for goal-based activity detection

Abstract

Human activity detection from video that is recorded continuously over time has been gaining increasing attention due to its use in applications like security monitoring, smart homes and assisted living setups. The analysis of continuous videos for the detection of specific activities, called Activities of Interest (AoI) in this work, is particularly challenging, as the start and end times of the AoI are unknown, while the AoI themselves feature large anthropometric variations, making their recognition more difficult. Continuously recorded videos also contain periods of inactivity, or activities that are not in the set of AoI, further complicating the problem of detection.This work attempts to overcome these challenges by introducing the concepts of (1) discriminative descriptors, (2) a goal for the AoI, represented by the most discriminative descriptors, (3) a novel, goal-based framework for activity detection and recognition in video. We represent AoI goals by descriptors found to be the most discriminative, as defined by a function of their correlation distance from the majority of the data, rather than by semantics or parametric modeling. This ensures flexibility, as activities in video feature many variations which cannot always be adequately represented by model-based approaches. Temporal detection of AoI is based on the distance of the test data from the AoI goals, which is shown to provide accurate results. Activity recognition takes place by applying SVM classification on the detected AoI, and the results are compared with the state-of-the-art on publicly available real world and benchmark research datasets.