By Pier Luigi Mazzeo, Paolo Spagnolo, Thomas B. Moeslund
This publication constitutes the completely refereed post-conference complaints of the second one overseas Workshop on task tracking by way of a number of dispensed Sensing, AMMDS 2014, held in Stockholm, Sweden, in August 2014, as a satellite tv for pc occasion of ICPR 2014, the twenty second foreign convention on development attractiveness. The nine revised complete papers incorporated within the quantity examine the demanding situations that come up while disbursed sensor networks are used to trace, computer screen, and comprehend the task, cause, and causes of people. program parts contain human-computer interplay, person interface layout, robotic studying, and surveillance.
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Additional info for Activity Monitoring by Multiple Distributed Sensing: Second International Workshop, AMMDS 2014, Stockholm, Sweden, August 24, 2014, Revised Selected Papers
O’Reilly, Cambridge (2008) 35. : Automatic generation and detection of highly reliable ﬁducial markers under occlusion. Pattern Recogn. 47, 2280–2292 (2014) 36. : A statistical approach for real-time robust background subtraction and shadow detection. In: Proceedings of IEEE ICCV, pp. 1–19 (1999) 37. : Shape from silhouette using dempster shafer theory. Pattern Recogn. 43, 2119–2131 (2010) Topological Features for Monitoring Human Activities at Distance Javier Lamar Leon1(B) , Ra´ ul Alonso1 , Edel Garcia Reyes1 , and Rocio Gonzalez Diaz2 1 Patterns Recognition Department, Advanced Technologies Application Center, 7th A # 21406 e/ 214 y 216, Rpto.
Smart Environ. 1(3), 235–259 (2009) 23. : The PERSONA service platform for AAL spaces. C. ) Handbook of Ambient Intelligence and Smart Environments, pp. 1171–1199. Springer, New York (2010) 24. : Propagating constraints on sets of intervals. In: ICAPS Workshop on Planning and Scheduling with Timelines (PSTL) (2012) 25. : A scalable approach to activity recognition based on object use. es Abstract. In this paper, we introduce a new multi-view dataset for gait recognition. The dataset was recorded in an indoor scenario, using six convergent cameras setup to produce multi-view videos, where each video depicts a walking human.
Fig. 5. GEI and GEnI. The leftmost image shows a walking subject. The reconstructed volumes are aligned along the gait sequence, as can be seen in the second image. The two last images show the GEI and GEnI computed over rendered images of the aligned sequence, respectively. We designed a hold-out experiment where the gallery set is composed by the 1st, 2nd, 4th, 5th, 7th and 8th sequences and probe set is formed by 3rd, 6th, and 9th sequences of the AVA Multi-View dataset. The recognition rate The AVA Multi-View Dataset for Gait Recognition 35 obtained with the application of the gait descriptors proposed in [7,9] are shown in Table 3.