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This project introduces a novel approach to multi-camera people tracking, leveraging anchor-guided clustering and spatio-temporal consistency ID re-assignment to impro...


This GitHub project presents an innovative methodology designed to enhance the accuracy and efficiency of tracking individuals across multiple camera feeds. At the core of this approach are two key techniques: anchor-guided clustering and spatio-temporal consistency ID re-assignment.

Anchor-guided clustering operates by utilizing anchors, which are predefined reference points or features within the data, to organize and group the tracked individuals. This method helps in accurately distinguishing between different individuals in complex environments, where traditional tracking methods might struggle due to overlapping paths or similar appearances. By guiding the clustering process with anchors, the system can more reliably associate the correct identity with each individual, even in densely populated or dynamically changing scenes.

Spatio-temporal consistency ID re-assignment is a technique aimed at maintaining the identity of each individual across different frames and camera views. This is particularly challenging in multi-camera setups due to varying angles, lighting conditions, and occlusions. The system uses spatial (location-based) and temporal (time-based) information to re-assign IDs to individuals, ensuring that a person tracked in one camera feed is consistently identified across all other feeds. This re-assignment process relies on understanding the spatial layout of the environment and the temporal sequence of movements, thereby reducing identity switches and loss of track.

By combining these two techniques, the project aims to solve some of the most challenging aspects of multi-camera people tracking. The anchor-guided clustering improves the initial detection and differentiation of individuals in a scene, while the spatio-temporal consistency ID re-assignment ensures that once an individual is identified, they are tracked consistently and accurately across all camera feeds over time. This dual approach not only enhances tracking accuracy by reducing errors in identity assignment but also increases efficiency by streamlining the process of tracking individuals in complex, multi-camera environments.

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