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

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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.

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Notes

  1. See Sect. 5 for more details on the data gathering process.

  2. The dataset has been made publicly available at http://www.cs.ucf.edu/~smasood/datasets/UCFKinect.zip.

  3. The optimal value of the threshold T was found for each value of γ using the training set.

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Acknowledgements

Marshall F. Tappen, Syed Z. Masood and Chris Ellis were supported by NSF grants IIS-0905387 and IIS-0916868. Joseph J. LaViola Jr. was supported by NSF CAREER award IIS-0845921 and NSF awards IIS-0856045 and CCF-1012056.

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Authors and Affiliations

  1. Department of Computer Science, University of Central Florida, Orlando, FL, 32826, USA

    Chris Ellis, Syed Zain Masood, Marshall F. Tappen & Joseph J. LaViola Jr.

  2. Google Research, Google Inc, Mountain View, CA, 94043, USA

    Rahul Sukthankar

Authors
  1. Chris Ellis
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  2. Syed Zain Masood
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  3. Marshall F. Tappen
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  4. Joseph J. LaViola Jr.
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  5. Rahul Sukthankar
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Correspondence to Syed Zain Masood.

Additional information

S.Z. Masood and C. Ellis contributed equally towards this paper.

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Ellis, C., Masood, S.Z., Tappen, M.F. et al. Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition. Int J Comput Vis 101, 420–436 (2013). https://doi.org/10.1007/s11263-012-0550-7

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