Linear-time Online Action Detection From 3D Skeletal Data Using Bags of Gesturelets

TL;DR

This paper introduces a fast, linear-time online action detection method using 3D skeletal data that is suitable for real-time applications and invariant to temporal scale changes.

Contribution

It presents a novel online detection approach that efficiently identifies action intervals in 3D skeletal data with linear complexity, enabling real-time performance.

Findings

Achieves linear-time detection suitable for real-time applications

Invariant to temporal scale variations

Effective in identifying action intervals in unsegmented streams

Abstract

Sliding window is one direct way to extend a successful recognition system to handle the more challenging detection problem. While action recognition decides only whether or not an action is present in a pre-segmented video sequence, action detection identifies the time interval where the action occurred in an unsegmented video stream. Sliding window approaches for action detection can however be slow as they maximize a classifier score over all possible sub-intervals. Even though new schemes utilize dynamic programming to speed up the search for the optimal sub-interval, they require offline processing on the whole video sequence. In this paper, we propose a novel approach for online action detection based on 3D skeleton sequences extracted from depth data. It identifies the sub-interval with the maximum classifier score in linear time. Furthermore, it is invariant to temporal scale variations and is suitable for real-time applications with low latency.