Outline Proposal - Oxford Brookes University

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Request 69009 Page 2 of 11 KTPs with HedgeVantage (a financial consulting company), Webmart, and Magna International (the multinational car component company). See our websites for more info. • Other information (sponsors, award, etc.): See our websites or call us for more info. Submission Terms By placing an “X” in the box below, I verify that I am submitting only non-confidential information. Further I agree to notify NineSigma, should this proposal result in a transaction with NineSigma's customer. (This effort is to ensure proper record keeping). I agree to NineSigma’s submission terms x Please insert your text below each heading in the form below, expanding as needed. Additional guidelines for preparing your proposal are included on the last page of this document. 23611 Chagrin Blvd., Suite 320, Cleveland, OH 44122 • 216-295-4800 • www.ninesigma.com
Request 69009 Page 3 of 11 Title of <strong>Proposal</strong>: Locating and recognizing complex activities using part-based discriminative models Proposed Technical Approach Please include the following information by selecting options or giving brief description, possibly by using graph, figure, or drawing. Please append a copy of your paper pertaining to proposed technology. Category of proposed technology (please select a relevant one) Technology developed/optimized for monitoring people, in particular the localization and recognition of actions and complex activities by multiple actors Development stage (please select a relevant one) Under verification and improvement in both the lab and the field Overview of proposed technology (1) Analysis/recognition algorithm (what kind of human activities/behaviors can be analyzed in what processing steps): Action recognition is a hard problem for a number of reasons: #1 human motions possess a high degree of inherently variability, as quite distinct motions/gestures can carry the same meaning. As action models are typically learned from forcibly limited datasets (in terms of training videos and action classes) they have limited generalization power; #2 actions are subject to various nuisance or “covariate” factors, such as illumination, moving background, viewpoint, and many others (Figure 1). Tests have been often run in small controlled environments, while few attempts have been made to progress towards recognition “in the wild”; #3 detecting when and where an action takes place within a video is the first step in any action recognition framework: so far, however, the focus has largely been on the recognition of pre-segmented videos; #4 the presence of multiple actors (e.g., different players sitting in front of a single console) greatly complicates both localization and recognition; #5 a serious challenge arises when we move from simple, “atomic” actions to more sophisticated “activities”, series of elementary actions connected in a meaningful way common, for instance, in the smart home scenario. Figure 1: numerous nuisance factors, e.g. view, illumination, occlusions, multiple actors make the activity recognition problem hard. The most successful recent approaches, which mainly adopy kernel SVM classification of bags of local features (Figure 2), have reached their limits: only understanding the spatial and temporal structure of human activities can help us to successfully locate and recognize them in a robust and reliable way. 23611 Chagrin Blvd., Suite 320, Cleveland, OH 44122 • 216-295-4800 • www.ninesigma.com
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Outline Proposal - Oxford Brookes University

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