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Fig. 5 Squash HPaSMM with two upper level states corresponding to activity phases S 1 and
S 2 . Each of these upper level states is characterized by three HMMs (modeling respectively
˙
S i
1
S i
1 and d S i for the considered state S i ) and by a GMM modeling sd i ( i.e. , the state duration
density in the upper level state S i ). Upper level layer is surrounded by red square and lower
level layer is surrounded by green squares.
, ˙
γ
γ
Lower layer: feature modeling using parallel hidden Markov models
Tackling with the lower layer modeling, the PaHMM modeling proposed in [25] has
been used to build a probabilistic modeling of the spatio-temporal behavior of the
feature vectors ˙
S i 2 and D S i . Figure 5 presents the lower level layer surrounded
in green and composed of 3-layer PaHMMs.
S 1 , ˙
γ
γ
3.3
Considered Data Sets and Experiments
The proposed temporal segmentation framework has been tested on trajectories from
squash videos taken from the “CVBASE'06” sports video database [3]. It provides
squash videos as well as trajectories of the squash players in the images. Game
activity phases (“rally” and “passive” phases) that we use as ground truth for ex-
perimental evaluations are also available. An illustration is given in Figure 6 which
shows two frames from the processed squash video. According to ground truth, the
left one corresponds to “rally” activity and the right one to “passive” activity.
Trajectories of the first half of the squash video (corresponding to 7422 frames)
were used for training a HPaSMM with two states S 1 and S 2 corresponding to activ-
ity phases “rally” and “passive”. Trajectories of the second half (corresponding to
8086 frames) of the video were used to test the proposed modeling. Fig. 7 presents
the squash players trajectories of the squash videos respectively used for training
and testing.
The activity phase denoted by “rally” is defined by the period between the be-
ginning of a point (a player serves) and its end (when ball bounces twice or when a
 
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