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The quality of a crowd simulation model is determined by its agents' local and global trajectory efficiency. While an agent-based model can accurately handle the local trajectories, global decisions usually are handled by a global path planner. However, most of the global path planning techniques do not consider other agents and their possible paths and the future global flow in the environment. In this paper, we propose a composite system that takes future agent configurations into account via a modified A* algorithm to create a global path plan and combines the global path plan with a local navigation model. We show that the agents using the proposed model intelligently plan their paths based on the dynamic configuration of the environment. In order to balance the performance vs. trajectory quality trade-off, we propose a hierarchical grid structure and discuss its effects on both trajectory quality and computational performance.

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Creating an interactive simulation of a large urban environment populated with virtual humans poses a number of interesting challenges, ranging from how to initialise the virtual humans in the correct locations to maintaining a real-time simulation. When considering a large environment it is beneficial to investigate automatic techniques that create and simulate virtual humans using the available computing resources effectively. In this paper, two contributions towards the population of a large environment are described. The first presents two methods that use an automatic analysis of the urban environment to determine the required population densities throughout the scene. These methods ensure that the virtual humans are distributed such that main high streets consistently exhibit a higher number of virtual humans than other areas. The methods presented here achieve above 90% correlation to the predicted population densities for the environment, which outperforms the state of the art. The second contribution concerns the optimisation of the two methods to facilitate their applicability to large environments. The methods presented are modified to insert virtual humans dynamically into the behaviour system when required. The approach limits the required computing resources whilst achieving above 75% correlation to the predicted population values.

This study addresses the issue of silhouette extraction of a street, and proposes two novel approaches to overcome this problem. The first, namely hybrid-stitching, considers the silhouette extraction as an image stitching problem and aims to use 2D street view images. The algorithm used in this method integrates a new composition technique into a conventional image stitching pipeline. The developed software using the proposed hybrid approach results in better stitching performances when compared with the popular stitching tools in the literature. Despite the results of the proposed method are better than the state-of-the-art image stitching techniques in many cases, they are not reliable enough to handle all of the street view image sets. Accordingly, a second solution has been proposed, including 3D location information, namely, 3D Silhouette Extraction Pipeline. The pipeline involves several techniques and post-processing steps to handle both the transformation and projection of the obtained point cloud, and the elimination of misleading location information.The results reveal that compared with the 2D solutions, the proposed algorithm is very effective and more reliable in silhouette extraction of a street, which is critical in urban transformation and environmental protection.