The thesis performs an analysis on visuospatial complexity of dynamic scenes, and morespecifically driving scenes in the propose of gaining a knowledge on human visual perception of the visual information present in a typical driving scene. The analysis and measurement of visual complexity is performed by utilizing two different measure modelsfor measuring visual clutter, Feature congestion clutter measure [1] and Subband entropyclutter measure[1] introduced by Rosenholtz, a cognitive science and research. The thesisrepresent the performance of the computational models on a data set consisting of sixepisodes that simulate driving scenes with different settings and combination of visualfeatures. The results of evaluating the measure models are used to introduce a formulafor measuring visual complexity of annotated images by extracting valuable informationfrom the annotated data set using Scalabel[2], an annotation web- based open source tool.

Visuospatial complexity refers to the level of detail or intricacy present within a scene, takinginto account both spatial and visual properties of the dynamic scene or the place (e.g.moving images, everyday driving, video games and other immersive media). There havebeen several studies on measuring visual complexity from various viewpoints, e.g. marketing,psychology, computer vision and cognitive science. This research project aims atanalysing and evaluating different models and tools that have been developed to measurelow-level features of visuospatial complexity such as Structural Similarity Index measurement,Feature Congestion measurement of clutter and Subband Entropy measurement ofclutter. We use two datasets, one focusing on (reflectional) symmetry in static images,and another that consists of real-world driving videos. The results of the evaluation showdifferent correlations between the implemented models such that the nature of the sceneplays a significant role.