Title

Exploring The Trade-Off Between Accuracy And Observational Latency In Action Recognition

Keywords

Action recognition; Bag of words; Computational latency; Conditional random field; Microsoft Kinect; Multiple instance learning; Observational latency

Abstract

An important aspect in designing interactive, action-based interfaces is reliably recognizing actions with minimal latency. High latency causes the system's feedback to lag behind user actions and thus significantly degrades the interactivity of the user experience. This paper presents algorithms for reducing latency when recognizing actions. We use a latency-aware learning formulation to train a logistic regression-based classifier that automatically determines distinctive canonical poses from data and uses these to robustly recognize actions in the presence of ambiguous poses. We introduce a novel (publicly released) dataset for the purpose of our experiments. Comparisons of our method against both a Bag of Words and a Conditional Random Field (CRF) classifier show improved recognition performance for both pre-segmented and online classification tasks. Additionally, we employ GentleBoost to reduce our feature set and further improve our results. We then present experiments that explore the accuracy/latency trade-off over a varying number of actions. Finally, we evaluate our algorithm on two existing datasets. © 2012 Springer Science+Business Media, LLC.

Publication Date

2-1-2013

Publication Title

International Journal of Computer Vision

Volume

101

Issue

3

Number of Pages

420-436

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/s11263-012-0550-7

Socpus ID

84884910745 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84884910745

This document is currently not available here.

Share

COinS