Feature Encodings and Poolings for Action and Event Recognition: A Comprehensive Survey

Abstract

Action and event recognition in multimedia collections is relevant to progress in cross-disciplinary research areas including computer vision, computational optimization, statistical learning, and nonlinear dynamics. Over the past two decades, action and event recognition has evolved from earlier intervening strategies under controlled environments to recent automatic solutions under dynamic environments, resulting in an imperative requirement to effectively organize spatiotemporal deep features. Consequently, resorting to feature encodings and poolings for action and event recognition in complex multimedia collections is an inevitable trend. The purpose of this paper is to offer a comprehensive survey on the most popular feature encoding and pooling approaches in action and event recognition in recent years by summarizing systematically both underlying theoretical principles and original experimental conclusions of those approaches based on an approach-based taxonomy, so as to provide impetus for future relevant studies.