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Methods: First, 8 male subjects had to look for specific female targets within a heavily cluttered public area. Subjects were supported by differing amounts of markings that helped them to identify females in general. We presented video clips and analyzed the search patterns. Second, 18 subject matter experts had to identify targets on a heavily frequented motorway intersection. We presented them with video material from a UAV (Unmanned Aerial Vehicle) surveillance mission. The video image was subdivided in three zones: The central zone (CZ), a circle area of 10°. The peripheral zone (PZ) corresponding to a 4:3 format and the hyper peripheral zone (HPZ) which represented the lateral region specific to the 16:9 format. We analyzed fixation densities and task performance. Results: We found an approximately U-shaped correlation between the number of markings in a video and the degree of structure in search patterns as well as performance. For the motorway surveillance task we found a difference in mean detection time for CZ vs. HPZ (p=0.01) and PZ vs. HPZ (p=0.003) but no difference for CZ vs. PZ (p=0.491). There were no differences in detection rate for the respective zones. We found the highest fixation density in CZ decreasing towards HPZ. Conclusion: We were able to demonstrate that markings could increase surveillance operator performance in a cluttered environment as long as their number is kept in an optimal range. When performing a search task within a heavily cluttered environment, humans tend to show rather erratic search patterns and spend more time watching central picture areas. |
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CITATIONS
Cited by 4 scholarly publications and 2 patents.
Video surveillance
Video
Surveillance
Target detection
Unmanned aerial vehicles
Visualization
Fourier transforms
