Vector Space-based Estimation of Pose and Trajectory for Humans and Objects

Introduction:

Understanding the position, orientation, and movement of humans and objects is important for many applications, like robotics, augmented reality, and human-computer interaction. This project aims to develop a system that uses advanced techniques to estimate the position and movement of people and objects in dynamic environments.

Project Objectives:

Data Collection: Gather data using various sensors, such as cameras, depth sensors, and IMUs (Inertial Measurement Units), to capture information about people, objects, and their surroundings.

Feature Extraction: Create algorithms to identify useful features from the sensor data, such as key points on humans and objects, object outlines, and depth information.

Object Detection and Tracking: Develop algorithms to detect and track people and objects in real-time, even in challenging situations like occlusions or when multiple things are moving at once.

Pose Estimation: Use vector space techniques to estimate the position and orientation of people and objects, incorporating sensor fusion methods to improve accuracy.

Trajectory Reconstruction: Build methods to track the movement of people and objects over time, using prediction techniques like Kalman filters or particle filters.

3D Mapping: Create a 3D map of the environment, showing the positions and shapes of objects. Update the map as new data is received to keep it current.

Visualization and User Interface: Develop an easy-to-use tool to visualize the estimated positions and movements in real-time, allowing users to explore the data and analyze movement patterns interactively.

Performance Evaluation: Define metrics to assess the accuracy and robustness of the system, and run experiments in different scenarios to evaluate its performance.

Expected Outcomes:

A system capable of estimating the position and movement of people and objects in real-time, even in complex environments.

Demonstrated reliability and accuracy in tracking and reconstructing movement patterns.

Potential applications in areas such as robotics, augmented reality, surveillance, and human-robot interaction.

This project aims to contribute to advancements in pose and movement estimation techniques, which can benefit fields like robotics, computer vision, and interactive systems. The outcomes will help create smarter, more adaptable technologies that can better understand and respond to their surroundings.