By Erez Berkovich, Hillel Pratt, Moshe Gur (auth.), Barbara Caputo, Markus Vincze (eds.)
This quantity constitutes the post-conference court cases of the 4th foreign Cognitive imaginative and prescient Workshop, ICVW 2008, held in Santorini, Greece, on may possibly 12, 2008.
The eleven papers awarded have been rigorously reviewed and chosen from 17 submissions. They hide vital features of cognitive imaginative and prescient like face reputation, job interpretation, realization, reminiscence maps and scene interpretation.
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Additional info for Cognitive Vision: 4th International Workshop, ICVW 2008, Santorini, Greece, May 12, 2008, Revised Selected Papers
To attain this goal we have set up an experimental laboratory (Gerhome laboratory) to analyze and evaluate our approach. This laboratory is located in the CSTB (Centre Scientiﬁque de Techniques du Batiment) at Sophia Antipolis. It looks like a typical apartment of an elderly person: 41m2 with entrance, livingroom, bedroom, bathroom, and kitchen. The kitchen includes an electric stove, microwave oven, fridge, cupboards, and drawers. 4 video cameras are installed in Gerhome laboratry. One video camera is installed in the kitchen, two video cameras are installed in the livingroom and the last one is installed in the bedroom to detect and track the person in the apartment and to recognize her/his postures.
Misdetected) region. ﬁeld of view) than the real camera. The 3D human silhouettes are then extracted and compared with the detected silhouette using a 2D techniques (projection of the silhouette pixels on the horizontal and vertical axes, see ﬁgure 3). Ro (H) = i∈Io (Hid −Hia )2 d 2 i (Hi ) , Rm (H) = i∈Im (Hid −Hia )2 a 2 i (Hi ) (1) (α1 , α2 , β1 , β2 ) ∈ [0, 1]4 , α1 + α2 + β1 + β2 = 1 The distance from the detected silhouette to the model silhouette is computed with the equation (2): dist(Sa , Sd ) = α1 Ro (H) + β1 Rm (H) + α2 Ro (V ) + β2 Rm (V ) (2) The most similar extracted 3D silhouette corresponds to the current posture of the observed person.
3D visualization of the recognition of the “falling down” event. (a) person is standing. (b) sitting with ﬂexed legs. (c) lying with outstretched legs. 5 Conclusions and Future Works In this paper we have described a cognitive vision approach to recognize a set of activities of interest of elderly at home by using ten 3D key human postures. This approach takes as input only video data and produces as output the set of the recognized activities. The ﬁrst contribution of this work consists in the identifying and modeling of the ten 3D key human postures.