What does this research mean for the field?

A modular, decoupled pipeline for semantic video indexing and person-centric retrieval, utilizing a fine-tuned SigLIP2 model, achieves state-of-the-art mean Average Precision on the RSTPReid benchmark. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To create a modular and scalable pipeline for semantic video indexing and person-centric retrieval that integrates real-time tracking, multimodal embeddings, and structured metadata storage.

September 2, 2026Multimedia Tools and ApplicationsOpen Access

A scalable and semantic pipeline for efficient video indexing and person retrieval

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Authors

RHRashid Hmaidan SMSebastiano MilardoUniversity of Catania IDIrene DonatoUniversity of Alberta

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Implication

Computational evaluation demonstrates improved person retrieval accuracy across video benchmarks, suggesting scalable vision-language pipelines enhance real-time surveillance indexing.

Key Points

To create a modular and scalable pipeline for semantic video indexing and person-centric retrieval that integrates real-time tracking, multimodal embeddings, and structured metadata storage.
Coupled real-time person detection and tracking using YOLO11 and BoT-SORT with face recognition using InsightFace and multimodal feature extraction using SigLIP2.
Fine-tuned the SigLIP2-SO400M-Patch14-384 vision-language model on a curated mixture of person-centric image-text datasets.
Evaluated retrieval performance across text-to-person queries against recent search methods using the RSTPReid benchmark.
Achieved a mean Average Precision (mAP) of 0.68 on the RSTPReid benchmark, significantly outperforming the previous best competing baseline of 0.54.
Demonstrated competitive Recall@k retrieval accuracy for text-based person searches within the modular architecture.

Cite This Study

Hmaidan et al. (2026) studied this question.

synapsesocial.com/papers/6a97e2b1c562ede874ec6eda https://doi.org/10.1007/s11042-026-21882-7

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Implication

Computational evaluation demonstrates improved person retrieval accuracy across video benchmarks, suggesting scalable vision-language pipelines enhance real-time surveillance indexing.

A scalable and semantic pipeline for efficient video indexing and person retrieval

Discussion

Member takes

Implication

Key Points

Cite This Study

A scalable and semantic pipeline for efficient video indexing and person retrieval

Discussion

Member takes

Implication

Key Points

Cite This Study

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