Additional Examples for Hybrid Model Adaptation

This section includes a number of additional sequences from the outdoor dataset, demonstrating the performance of gait model extraction using the hybrid model adaptation approach.

Figure 42: Hybrid model adaptation, sequence ‘008e014s00L’

Figure 42 shows some significant errors in extraction of the head and lower leg in the first part of this sequence. However, the gait signature derived for recognition (Section 6.5) is an average measure of body shape and gait motion, meaning that local errors will not significantly obstruct the recognition process, provided that they constitute only a small proportion of the sequence.

Figure 43: Hybrid model adaptation, sequence ‘008e015s00L’

Two additional sources of error may be observed in Figure 43. The subject’s trousers are somewhat difficult to distinguish from the background, as no colour information is employed in the extraction process. The folds and shadows in the trousers also create additional edges that the leg contours may be attracted to. There is a tracking failure towards the end of the sequence, possibly because the subject slows down, or possibly due to the lack of edge data in this heavily shadowed region.

Figure 44 depicts a generally good model extraction. The most significant error apparent in this example is the placement of the hip joints in the support phase of the gait cycle. There are also some errors in foot placement and pose in this sequence.

Figure 45: Hybrid model adaptation, sequence ‘009e017s00L’

Figure 45 demonstrates one of the advantages of including a global modelling strategy. In frame 20, the position and pose of the subject is extracted with surprising accuracy, even though the subject is almost completely occluded by another pedestrian. Errors in extraction of the lower leg are more numerous in this sequence, which indicates that the local maximisation process is unable to correct the degree of error present in the global initialisation, or that the local edge data available is unreliable.

Figure 46: Hybrid model adaptation, sequence ‘009e018s00R’

Extraction of the head contours is unreliable in Figure 46, and some difficulties are encountered in dealing with the bare legs of the subject. However, joint dynamics are generally extracted with a high degree of accuracy.

Figure 47: Hybrid model adaptation, sequence ‘012e031s00L’

The long, flowing robes of the subject in Figure 47 present a serious problem for gait extraction algorithms, as a large proportion of the leg region is obscured. This sequence is also heavily shadowed, increasing the difficulty of the extraction task. However, enough variation is visible to extract periodicity information in this sequence, which allows a reasonable approximation of the subject’s gait. It is unclear in general what proportion of the legs must be visible to allow gait extraction, but even if the legs were completely obscured, the motion of the clothes may be sufficient to extract some gait information. Of

or trench coat. course, these comments apply equally to subjects wearing a long skirt

Figure 48: Hybrid model adaptation, sequence ‘012e032s00L’

igure 48 demonstrates the resulting confusion when the subject is forced to slow down to F

avoid a collision with a group of people blocking the path. This violates the constant velocity assumption made by the COM tracking algorithm (Section 3.2.1), and prevents the computation of an accurate temporal accumulation. This in turn means that the model adaptation algorithm is poorly initialised, and an acceptable gait description cannot be extracted. This problem is solvable with a more sophisticated tracking algorithm, which would be required for more general application scenarios (see Section 7.2).

Figure 49: Hybrid model adaptation, sequence ‘012e034s00R’

Figure 49 shows a heavily shadowed sequence in which there is little contrast between the subject’s legs and the ground. This leads in some cases to errors in leg pose determination, but the upper body is extracted well.

The subject in Figure 50 is similarly in heavy shadow, although there are no major errors apparent in this extraction.

Figure 51: Hybrid model adaptation, sequence ‘012e036s00L’

Figure 51 shows a successful extraction despite the bus passing behind the subject, demonstrating that correlation with mean human shape is an effective means of distinguishing the subject from other moving objects (see Equation 12, Section 3.2.1). The

e width of the upper body, which is overestimated to some the bus.

only obvious error here is in th

Figure 52: Hybrid model adaptation, sequence ‘013e037s00R’

The sky is clouded over in Figure 52, so there are no significant shadows in the foreground e errors in the suggests an additional problem related to the assumption of a

shape parameters are of this scene. Although most of the model is extracted well, there are som

localisation of the feet. This

flat ground plane. Although the hybrid model adaptation approach can correct the y- position of the COM, the height of the subject is assumed to be constant. When the ground plane is not flat height is likely to be over-estimated, as the temporal accumulation will be spread out over the y-plane. This problem could be solved by the inclusion of height as an additional free parameter in the adaptation process, but as all other

dependent on the subject’s height, this may require too much additional computation. Again, relaxing the assumptions made on the motion of the subject and employing a more sophisticated COM tracking algorithm would alleviate this problem.