Tomass Zutis, Peteris Racinskis, Anzelika Bureka, Janis Judvaitis, Janis Arents, and Modris Greitans. Multi-Step Object Re-Identification on Edge Devices: A Pipeline for Vehicle Re-Identification. Charting the Intelligence Frontiers – Edge AI Systems Nexus, 233-253 pp. River Publishers, 2025.
Bibtex citation:
Bibtex citation:
@inproceedings{17880_2025,
author = {Tomass Zutis and Peteris Racinskis and Anzelika Bureka and Janis Judvaitis and Janis Arents and and Modris Greitans},
title = {Multi-Step Object Re-Identification on Edge Devices: A Pipeline for Vehicle Re-Identification},
journal = {Charting the Intelligence Frontiers – Edge AI Systems Nexus},
pages = {233-253},
publisher = {River Publishers},
year = {2025}
}
author = {Tomass Zutis and Peteris Racinskis and Anzelika Bureka and Janis Judvaitis and Janis Arents and and Modris Greitans},
title = {Multi-Step Object Re-Identification on Edge Devices: A Pipeline for Vehicle Re-Identification},
journal = {Charting the Intelligence Frontiers – Edge AI Systems Nexus},
pages = {233-253},
publisher = {River Publishers},
year = {2025}
}
Abstract: In modern computer vision tasks, the ability to identify and track objects
across different scenes and environments has become important for numerous applications, especially in transportation. Inspired by this need, we propose a method that leverages a multi-step process focused on extracting and using object features for object re-identification. The proposed pipeline includes the following steps: detecting an object, converting its features into a vector embedding, storing this embedding in a vector database, and then querying the database to find the same or similar objects based on their feature embeddings. This approach enables us to identify the same object across different images or cameras, even in varying locations. This is essential in scenarios like Vehicle Re-Identification. For such scenario, implementing this process on edge devices is crucial. Therefore, ways to tailor the pipeline and its outputs for edge devices are outlined. The paper details the pipeline’s structure along with the experimental setup demonstrating its application, particularly in vehicle re-identification. The pipeline achieves 70-80% re-identification precision when dealing with vehicle images from our network cameras and above 70% Rank-1 accuracy when dealing with a CityFlow video track scenario.