Online Action Representation using Change Detection and Symbolic Programming

TL;DR

This paper introduces an online action representation method that uses change detection and symbolic programming to segment, interpret, and count repetitions of human actions in streaming videos, outperforming some existing offline methods.

Contribution

It presents a novel online approach combining change detection and symbolic primitives for action segmentation and interpretation, enabling real-time repetition counting.

Findings

Effective online segmentation of actions using change detection.

Higher-level semantic representation through symbolic primitives.

Competitive or superior performance compared to offline methods.

Abstract

This paper addresses the critical need for online action representation, which is essential for various applications like rehabilitation, surveillance, etc. The task can be defined as representation of actions as soon as they happen in a streaming video without access to video frames in the future. Most of the existing methods use predefined window sizes for video segments, which is a restrictive assumption on the dynamics. The proposed method employs a change detection algorithm to automatically segment action sequences, which form meaningful sub-actions and subsequently fit symbolic generative motion programs to the clipped segments. We determine the start time and end time of segments using change detection followed by a piece-wise linear fit algorithm on joint angle and bone length sequences. Domain-specific symbolic primitives are fit to pose keypoint trajectories of those extracted segments in order to obtain a higher level semantic representation. Since this representation is part-based, it is complementary to the compositional nature of human actions, i.e., a complex activity can be broken down into elementary sub-actions. We show the effectiveness of this representation in the downstream task of class agnostic repetition detection. We propose a repetition counting algorithm based on consecutive similarity matching of primitives, which can do online repetition counting. We also compare the results with a similar but offline repetition counting algorithm. The results of the experiments demonstrate that, despite operating online, the proposed method performs better or on par with the existing method.