complex event detection module. The construction of the model library is influenced by psychological and social crowd studies
and by crowd modeling techniques in order to achieve realistic scenario models. On the one hand, scenarios in the library
comprise simple and common group behavior which occurs in everyday life, like normal walking. On the other hand, scenarios
in the library comprise complex events which are uncommon and possibly dangerous, like a bottleneck situation. Complex
events are specified as group behavior that can occur when one or more groups of pedestrians interact with each other or with
objects. In the following, the scenario model library is described in detail.
The most common scenarios are three basic group motion characteristics, namely Standing, Walking and Running, which
are uniquely defined by the histogram of pairwise motion patterns and by constant speed- and density histograms.
Furthermore, the scenarios Standing and Walking serve as supplemental information for many of the following scenarios,
depending on the pace in which the specific scenario is carried out. The next common scenarios are Accelerating and Slowing
Down, which are uniquely defined by the change of the speed histogram. More complex scenarios are Individual Merge or
Individual Split and Group Merge or Group Split, depending on a change of connected components by one or more nodes in the
graph. The split and merge scenarios are also possibly supplemental or co-existent with other scenarios because groups
or individuals can separate or join continuously. A special case of a group merge or individual merge is the Frontal Collision
scenario, in which pedestrians interfuse each other. In this scenario, the histogram of the scalar of the motion vectors
uniquely notices a temporal rise of antiparallel moving pedestrians. Additionally, the histogram of pairwise motion
patterns reflects the increase of converging interactions in contrast to the normally walking interaction shortly before. The
formation of lanes at interfusing groups can be confirmed by still existing normally walking patterns between pedestrians of
common motion direction and a large amount of converging and diverging interactions. The formation of lanes can be observed
in narrow passages; therefore, this specific scenario is called Corridor. A possibly dangerous scenario is the Bottleneck
scenario, which is defined by an interim increase in the density histogram, a decrease in the speed histogram and change from
converging to diverging in the histogram of pairwise motion patterns. Also, an Escape of a walking or standing group is a
dangerous situation. This scenario is defined by a sudden increase in the speed histogram, simultaneous with a change in
the histogram of pairwise motion patterns from walking or standing towards diverging
and running interaction.
Furthermore, the corresponding connected component splits up into smaller connected components or into single pedestrians.
The scenario Brawl is an uncoordinated movement of a group in which several persons could possibly be fighting. Therefore,
features indicating uncoordinated movement are used to define a brawl, such as a high standard deviation of the speed
histogram and many different entries in the histogram of the scalar of the motion vectors.
4. EXPERIMENTAL RESULTS
We show experimental results for our complex event detection approach on the basis of a new UAV dataset. In the next
section, the UAV dataset is described in detail. Afterward, the experimental results are presented.
4.1. Dataset The evaluation of our complex event detection approach is
based on a new UAV dataset which was captured from an AscTec Falcon 8 octocopter. An UAV provides high mobility
and is an ideal platform for surveillance. The images were taken from a height of 85m with a Panasonic DMC-LX3 camera,
resulting in a ground resolution of about 1.5cm. The frame rate of the dataset is 1Hz, the images are robustly aligned using
Harris feature points. The UAV dataset contains more than 10 different scenarios representing group behavior in different
complexity levels in a controlled setting. The pedestrian group in the dataset consists of up to 18 persons who were instructed
in advance. The information given to the persons was reduced to a minimum in order to preserve natural behavior. Common
scenarios include parallel walking or running in a group. More complex scenarios include the interaction of a moving or
standing group with passing individuals or the interaction between two groups. Further scenarios additionally incorporate
interaction with obstacles or objects, such as a bottleneck Figure 5 or a corridor. Additionally, dangerous scenarios with
brawling or escaping groups are available. Table 1 gives a detailed summary of scenarios.
For the evaluation of the complex event detector we use reference trajectories which were generated by manually
tracking the colored hats in each frame of the image sequences. However, our approach is also able to deal with possibly
incomplete automatically generated tracklets because the graph can straightforwardly deal with changing topology.
Furthermore, pedestrians within a group usually behave like their surroundings and their missing detection does not distort
the general group-related features. For the density calculation an intermittent detection of a present person in a group can be
buffered such that the density can be kept over the sequence. This is due to the fact that a single person cannot disappear
within one frame. Hence, it is supposed to be still present.
Parallel group motion walking running Diverging walking running
Converging walking running Random walking
Individual crossing group walking running Groups crossing sidewardshead-on walking running
Group overtaking group Group passing wide gap walking running
Group passing small gap walking running Group passing corridor walking running
Groups passing corridor head-on walking running Group avoiding obstacle walking running
Groups brawling Group escaping “bomb” situation
Table 1. List of available scenarios in the UAV dataset.
Figure 5. Example taken from our new UAV dataset. Two frames within 4 seconds from the scenario “bottleneck through
small gap”.
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Figure 8 left.
Complex event detection result for the
scene “Groups passing corridor”. Frames 1, 5,
9, 13 and 16 are shown top to bottom.
Figure 6. Complex event detection result for the scene “Parallel group motion - walking”.
Frames 1, 4, 6, 9 and 12 are depicted top to bottom.
Figure 7. Complex event detection result for the scene “Individual crossing group”. Frames 1, 3, 5, 7, 8 are shown left to right.
Figure 9 top left. Complex event
detection result for the scene “Group passing
small gap”. Frames 1, 3, 6, 8 and 10 are
shown top to bottom. Figure 10 top right.
Complex event detection result for the
scene “group escaping bomb”. Frames 1, 2, 3,
4 and 5 are shown top to bottom.
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4.2. Complex event detection results The experimental results of our complex event detection
