## 5 Experiments and Results
### Data Fusion Specific Results Analysis and Lessons Learnt  
The INSTEAD--MIC data fusion success at the BCT is 93.4%, as described in [7]. Essentially, the time-based data fusion method described in Figure 6A has been challenged by: (1) the accuracy of the detection time of the MIC and 2D Video messages, i.e., the time when the object/person is at the init line (estimated in the case of MIC); (2) the fact that the relation of the messages 2D Video-MIC is one-to-many, i.e., one 2D Video message referring to a person may be related with more than one MIC message referring to the same weapon (possibly carried by that person); and (3) the time difference between the MIC detection and the 2D Video detection that, if too large, could lead to a missed data fusion. Thus, the risks of failures illustrated by the scenarios in Figure 13 should be considered when implementing such method. The first case, related to (1), shows the situation of a MIC message with detection time between those of two different persons detected by the 2D Video. In this case, the data fusion may result in an incorrect message association. The second case, related to (2), shows the situation when two detection messages of the same weapon by the sensor provide quite different (estimated) detection times so that, in case two persons are detected in between by the 2D Video, and the data fusion method may not uniquely identify the situation (e.g., two concurrent threats, carrying the same type of weapon, or just one threat detected by MIC more than once with increasing confidence level). The third case, related to (3), shows the situation when the detection times of the weapon and of the person is much different. Some control strategies were adopted during the BCT official trials to mitigate the occurrence of the scenarios above for the C&C. A minimum frequency between subsequent commuters to avoid MIC detections overlapping with 2D Video persons labelling was experimented during the runs and this resulted in about 2-3 s. This time was considered realistic for the given door-based deployment of MIC (Figure 1). Furthermore, accurate time synchronization of the servers hosting the individual components with the NTP server is fundamental to ensure that the detection time delivered by the two components inside the messages are coherent in this respect. Time synchronization was constantly monitored and occasionally manually forced during the trials when significant differences were observed. Network traffic is another influencing factor that might have caused late delivery of messages due to various additional clients connected to the broker for debugging purpose. Based on this experience, lessons learned for future work include the following. 1. Including and/or deriving more information from the field in the data fusion method while mantaing at least the accuracy demonstrated at the BCT. 2. Improving the time synchronization of the system components. 3. Activate a network monitoring activity to check connections and avoid unnecessary traffic. The EXTRAS--INSTEAD data fusion success at the BCT is 77.58%, as described in [7]. For this type of data fusion (identity-based, see Figure 6B), the C&C relies on a correct video tracking hand-over from the INSTEAD 3D Video to the video mounted on the sensor. However, in theory, a risk of missed data fusion by the C&C might be due to a sensor detection of a commuter not previously tracked. This problem has occurred in a few runs at the BCT for groups of commuters not clearly identifiable at the init line by the 2D Video system. As demonstrated by additional experiments reported in the Section 5.3,  
Figure 13: Critical scenarios for S1 & 2D Video (time-based) data fusion. this problem of missed 2D Video tracks might depend on occlusion or the disposition of the MIC panels before the init line (see also [7] for details) and so it could be faced by considering the following three possible enhancements of the software. 1.