**Synthetic Data Challenge: Realistic Motion Patterns in Urb

Synthetic Data Challenge: Realistic Motion Patterns in Urban Environments

Creating a robust and realistic dataset of pedestrian motion in dense urban environments is crucial for developing accurate pedestrian detection, tracking, and prediction systems. However, generating such a dataset can be challenging due to the complexity of interactions between pedestrians and the dynamic environment. To overcome this hurdle, let's create a synthetic dataset that incorporates complex interactions between pedestrians and dynamic street furniture.

Challenge Description

Design a synthetic dataset of pedestrian motion in a dense urban environment with the following characteristics:

1. Realistic Pedestrian Motion: Pedestrians with diverse characteristics (age, size, speed, and direction) move through the scene, interacting with each other and the environment.
2. Dynamic Street Furniture: Buses, bicycles, and other vehicles move through the scene, creating complex interactions w...

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