Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition

    Authors

    C. Ellis; S. Z. Masood; M. F. Tappen; J. J. LaViola;R. Sukthankar

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Int. J. Comput. Vis.

    Keywords

    Action recognition; Observational latency; Computational latency; Microsoft Kinect; Multiple instance learning; Conditional random field; Bag of words; POSE; Computer Science, Artificial Intelligence

    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.

    Journal Title

    International Journal of Computer Vision

    Volume

    101

    Issue/Number

    3

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    420

    Last Page

    436

    WOS Identifier

    WOS:000314719000003

    ISSN

    0920-5691

    Share

    COinS