Around 2,600 moving vehicles are recorded in The set of video sequences.
No differences between conditions were observed in monitoring (i.e. head rotation of approximately 45° or more towards the experimenter during the time of immobility) behaviour (fig. 2: number of monitoring sequences: Friedman's test, N = 16, P = 0.31; time spent monitoring the experimenter: Friedman's test, N = 16, P = 0.25).
More interesting is the finding that the effect of the person's familiarity appeared much stronger than the effect of her/his attentional state on horses' monitoring behaviour (GLM: Number of monitoring sequences: F1 123 = 21.81, P<0.0001; Time spent monitoring: F1 123 = 35.35, P<0.0001), whereas the "condition" factor did not reveal significant in the GLM procedure (P>0.1 for both variables).
Monitoring behaviour also differed between conditions (Friedman's test, N = 16, P = 0.04; fig. 2) with a clear increase in the "distracted" condition as compared to the "looking at" condition (number of monitoring sequences; Wilcoxon t-test, N = 16, t = 18, P = 0.03).
Figure 8 Comparison of the performance of the proposed system on the hall monitor sequence.
Figure 7 shows a sample frame from Hall-Monitor sequence before and after irreversible embedding at variable values of QP and.
Performance of the proposed system on the hall monitor sequence compared to the DISCOVER codec, the H.264 intra codec, and the FF system.
From Table 6, one can verify that the quality of the interpolated frames is slightly improved by the progressive scheme for the Hall monitor sequence.
For each mutation step (i.e. after each step of 10% change), we monitored sequence similarity to native, the relative content of residues predicted in short/long-disordered regions and the length of the regions (SOM).
In the performance results for the foreman, coastguard, soccer and hall monitor, sequences can be seen in Figures 5, 6, 7 and 8, respectively.
