I published the research achievements of this projects in two conference papers. Basically, the following findings are the important points of these papers which demonstrate academic significance:
i. Group recognition: The motion models developed for recognising pedestrian groups are applied to our dataset and it is demonstrated that the group recognition algorithm works with significant accuracy in the presence of strong flow direction, low-to-medium density and for multi-person groups.
ii. Interaction recognition: An interaction recognition stage is integrated into the group recognition method and it is demonstrated that considerable rates of performance are achieved.
iii. Indicators of various gestures: The indicators of coherent motion are investigated with respect to the gestures of interaction and it is demonstrated that different gestures yield different motion patterns.
iv. Variation on motion patterns with respect to interacting peers: The groups are analysed with respect to the interacting side (peer) and it is demonstrated that the peer who is active in interaction is positioned behind within 2-people (2p) groups to have his partner in the field of view. In addition, the not-interacting groups are observed to keep a more disperse distribution compared to interacting groups.