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Classifier Boosting for Human Activity Recognition

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Ensemble Machine Learning

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Abstract

The ability to visually infer human activities happening in an environment is becoming increasingly important due to the tremendous practical applications it offers [1]. Systems that can automatically recognize human activities can potentially help us in monitoring people’s health as they age [7], and to fight crime through improved surveillance [26]. They have tremendous medical applications in terms of helping surgeons perform better by identifying and evaluating crucial parts of the surgical procedures, and providing the medical specialists with useful feedback [2]. Similarly, these systems can help us improve our productivity in office environments by detecting various interesting and important events around us to enhance our involvement in important office tasks [21].

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  1. eBay Research Labs., 2145 Hamilton Ave, San Jose, CA, 95125, USA

    Raffay Hamid

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  1. Raffay Hamid
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Correspondence to Raffay Hamid .

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  1. Microsoft, One Microsoft Road, Redmond, 98052, USA

    Cha Zhang

  2. Honeywell, Douglas Drive North 1985, Golden Valley, 55422, USA

    Yunqian Ma

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Hamid, R. (2012). Classifier Boosting for Human Activity Recognition. In: Zhang, C., Ma, Y. (eds) Ensemble Machine Learning. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9326-7_9

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  • DOI: https://doi.org/10.1007/978-1-4419-9326-7_9

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