Project Proposal (PDF) - Oxford Brookes University

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FP7-ICT-2011-9 STREP proposal 18/01/12 v1 [Dynact] (iii) Detailed work description broken into work packages Work package No 1 Work package list Work package title Type of activity 2 Lead partic no. 3 Lead partic. short name Personmonths 4 Start month 5 End month 5 WP1 Imprecise-probabilistic generative dynamical modeling RTD 3 SYSTeMS 80 1 36 WP2 Classification of generative dynamical models RTD 2 IDSIA 72 1 36 WP3 Dynamical discriminative modeling RTD 1 OBU 39 1 24 WP4 Data collection and feature extraction RTD 4 SUP 42 1 18 WP5 Integration and testing in different scenarios RTD 1 OBU 66 13 36 WP6 Management and dissemination MGT 1 OBU 13 1 36 TOTAL OBU 312 1 Workpackage number: WP 1 – WP n. 2 Please indicate one activity (main or only activity) per work package: RTD = Research and technological development; DEM = Demonstration; MGT = Management of the consortium 3 Number of the participant leading the work in this work package. 4 The total number of person-months allocated to each work package. 5 Measured in months from the project start date (month 1). <strong>Proposal</strong> Part B: page [24] of [67]
FP7-ICT-2011-9 STREP proposal 18/01/12 v1 [Dynact] List of Deliverables Del. no. 6 Deliverable name WP no. Nature 7 Dissemi nation level 8 Delivery date 9 (proj.mon th) 1.1 Report on imprecise EM 1 R PP 6 6.1 Web site dedicated to the project 6 O PU 6 2.1 Report on similarity metrics for imprecise models 2 R PP 10 1.4 Report on specialization of imprecise EM to iHMMs 1 R PP 12 2.2 Software implementation of similarity metric b/w imprecise models 2 P PP 12 4.1 Proprietary action datasets of stereo sequences 4 O PU 12 4.2 Proprietary action datasets of range sequences 4 O PU 12 4.3 Data repository 4 O PU 12 5.1 First prototype of the overall action and activity localization and recognition framework 5 P PP 12 4.4 Library of routines for automatic robust discriminative feature extraction from monocular videos and synchronised 2D frames 4 P PP 15 2.4 Report on metric learning techniques for both precise and imprecise dynamical models 2 R PP 16 3.1 Report on MIL framework for action part learning 3 R PP 16 1.2 Report in inference algorithms for iHMMs 1 R PP 18 1.3 Software prototype of EM and inference algorithms 1 P PP 18 1.5 Software prototype of imprecise EM for iHMMs 1 P PP 18 2.3 Report on classification of imprecise graphical models via similarity measures 2 R PP 18 2.5 Software package on metric learning techniques 2 P PP 18 3.2 Software prototype of MIL framework 3 P PP 18 4.5 Library of routines for automatic robust discriminative feature extraction from range data 4 P PP 18 6.2 Intermediate report to the EU 6 R CO 18 3.3 Report on part based discriminative action models 3 R PP 24 3.4 Software implementation of discriminative action models 3 P PP 24 5.2 Preliminary report on classification/localization performances in all the available datasets and all scenarios 5 R PP 24 6.3 Special session(s) on the project organized at major international conferences 6 O PU 24 6.4 Special issue(s) on the project organized for major international journals 6 O PU 24 2.6 Report on SVM classification of imprecise models 2 R PP 28 6 Deliverable numbers in order of delivery dates. Please use the numbering convention .. For example, deliverable 4.2 would be the second deliverable from work package 4. 7 Please indicate the nature of the deliverable using one of the following codes: R = Report, P = Prototype, D = Demonstrator, O = Other 8 Please indicate the dissemination level using one of the following codes: PU = Public PP = Restricted to other programme participants (including the Commission Services). RE = Restricted to a group specified by the consortium (including the Commission Services). CO = Confidential, only for members of the consortium (including the Commission Services). 9 Measured in months from the project start date (month 1). <strong>Proposal</strong> Part B: page [25] of [67]
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