To further enhance the performance of the RotAvat framework in diverse video surveillance settings, several additional real-world constraints and assumptions could be integrated. One potential assumption is the presence of a known ground plane, which could be established through prior knowledge of the environment or through initial calibration. This would allow for more precise alignment of the 3D meshes to the ground, improving the accuracy of the pose and shape estimations.
Another constraint could involve the incorporation of human motion patterns and behaviors. By integrating models of typical pedestrian movement, such as walking speed and gait, the RotAvat framework could better predict and adjust the 3D poses of individuals in crowded environments. This could be particularly useful in scenarios with high levels of occlusion, where traditional methods may struggle to maintain accurate estimations.
Additionally, the framework could benefit from the integration of contextual information, such as the layout of the environment (e.g., obstacles, walls, and pathways). By understanding the spatial constraints of the scene, the RotAvat approach could refine its estimations to ensure that the predicted poses are not only accurate but also physically plausible within the given context.