期刊
INTERNATIONAL JOURNAL OF COMPUTER VISION
卷 101, 期 3, 页码 420-436出版社
SPRINGER
DOI: 10.1007/s11263-012-0550-7
关键词
Action recognition; Observational latency; Computational latency; Microsoft Kinect; Multiple instance learning; Conditional random field; Bag of words
资金
- NSF [IIS-0905387, IIS-0916868, IIS-0845921, IIS-0856045, CCF-1012056]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [0916868] Funding Source: National Science Foundation
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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