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FP7-SEC-2007-2.3-01 Grant No. 218004
 
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Threat Assessment

The methods developed to observe and analyse the situation provided information on the intentions and interactions of individuals within the scene and it was then the aim to develop a threat assessment subsystem to provide a mechanism for reasoning with this information to distinguish between threatening and benign situations.

The design of the threat assessment sub-system focussed around a rule based system which aimed to make it easy to encode the evolving state of the world and explore different behavioural patterns that constitute a potential threat, while maintaining a mechanism that was sufficiently general to accommodate external information (e.g. state of alert, time of day etc.).

The world model is constructed from a set of facts that describe the state of the surveyed scene at the current time-step and pertinent facts from the recent past. In the final SUBITO design, this model is continually refreshed with the current location of each tracked object and the group structure inferred using the SFM.

The development of the Threat Assessment subsystem was performed in three steps. Each of these steps is described below, and summarised in the following table.

Test Sequence Baseline Ownership Ownership + Social groups
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36 tick.png cross.png tick.png
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The first "baseline" system was designed to reproduce the performance equivalent to that achieved during the ISCAPS project. The second step added the concept of ownership while the third augmented the ownership concept with social relations.

The baseline system was implemented using the functionality previously explored in ISCAPS, where the threat (i.e. abandonment) was defined by two simple rules:

  • • Bag unattended if no person within 2 meters
  • • Bag abandoned if unattended for 30 seconds

The performance of the baseline subsystem was evaluated and the results are shown in the second column of the table below. The baseline definition fails to raise the correct alarm in all cases because there is always some individual in the surveyed scene that is sufficiently close to the baggage such that the alarm condition is never reached.

The second step involved implementation of an enhanced threat assessment which included the notion of ownership. In this case the rules were extended to:

  • • Bag owner is nearest person on appearance
  • • Bag unattended if owner is not within 2 meters
  • • Bag abandoned if unattended for 30 seconds

Thus for the threat assessment to be correct, the system was required to raise an alarm following a potential threat in addition to correctly identify both the ID of the abandoned bag and the ID of the owner. Again the results of the system evaluation are shown in table below. The Ownership definition still makes several errors, but performs better than the simple ISCAPS functionality.

The final step of the development was to augment the notion of ownership by incorporating inferred information on social relations into the threat assessment. This was achieved by running an "inverse-SFM" procedure at each time-step. This enables the system to recognise situations in which someone known to the owner (e.g. a friend or relative) is looking after a bag whilst the owner moves away. Without the knowledge of the social relationship, this situation would otherwise raise a false alarm. The threats are now defined as follows:

  • • Bag owner is nearest person on appearance
  • • Bag unattended if nobody from owner's group is within 2 meters
  • • Bag abandoned if unattended for 30 seconds

Once more the system was evaluated and the results are shown in the final column of the table. The results using the rules based on Ownership plus Social Groups again show an improvement in performance. Where an error did occur it was through failing to correctly assign two individuals to the same group; which in turn was due to insufficient evidence of their relationship prior to abandonment.

The simple reasoning engine used to implement the three versions of the logic outlined above was also used to correct three types of error in the output from image analysis and to improve the assignment of ownership.

  • • Occasionally changes in the ID of a person or bag due to temporary occlusion occurred, which was corrected by the implementation of a simple rule to detect such situations and substitute the original ID.
  • • Bags only appear in the scene when deposited by the owner. A bag appearing without an assigned owner was assumed to be spurious and removed from the world model.
  • • For many scenes, it can be assumed that individuals only appear close to the boundaries of the video frame. The system therefore assumed any person hypothesis that appears away from this boundary was spurious and removed it from the world model.

In the logic implemented for the tests reported above, the owner of a bag is the nearest person to the bag on appearance. Unfortunately, there were cases where the appearance of a bag was delayed due to the nature of the image analysis algorithm, such that a second individual was closer to the bag on appearance. A solution was implemented requiring that the person depositing a bag also paused at the location of its appearance. This additional constraint reduced the chance of an incorrect assignment.

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Executive Summary


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