Jul 2026· International Journal for Research in Applied Science and Engineering Technology· Vol 14, pp. 1586-1592· 0 citations
TL;DR
The results confirm that combining YOLOv8 with Deep SORT and a dedicated search layer yields a practical, extensible platform for real-time object detection, tracking, and retrieval applications.
Abstract
Real-time visual understanding has become a cornerstone of modern intelligent systems, spanning surveillance,
autonomous navigation, retail analytics, and smart-city infrastructure. The system architecture is described in detail, including
data-flow diagrams, use-case and sequence diagrams, an activity diagram, and a relational database design for persisting
detection and tracking metadata. An experimental evaluation performed on standard benchmark-style data demonstrates that the
proposed pipeline attains a mean Average Precision (mAP@0.5) of approximately 0.91, a tracking identity-switch rate reduced by
38% relative to a naive frame-by-frame detector, and a sustained throughput of 61 frames per second on a mid-range GPU,
outperforming several baseline architectures compared in this study. The results confirm that combining YOLOv8 with Deep
SORT and a dedicated search layer yields a practical, extensible platform for real-time object detection, tracking, and retrieval
applications.
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