arXiv Computer Vision
Bee Detection and Tracking at Hive Entrance using YOLO11 and ByteTrack
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Towards Practical Precision Agriculture: Real-Time Fruit Detection and Video Analytics on Embedded Edge Hardware
arXiv:2609.13551v1 Announce Type: new Abstract: Static-image benchmarks do not capture the computational and temporal requirements of practical orchard video analytics. This study presents an end-to-...
arXiv Machine Learning
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Ultra Low-Power, Lightweight, Probabilistic RSS-Based Path Reconstruction: A System for Landscape-Scale Bee Tracking
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arXiv Computer Vision
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BMCTrack-d: Pig re-identification and tracking via back marks in challenging camera settings
BMCTrack-d is a novel tracking-by-detection method that uses unique back marks on pigs to achieve robust re-identification and tracking in challenging side-view camera settings. The approach employs a neural network-based back mark classifier followed by temporal consistency checks and deduplication to improve identity reliability over time. On a demanding test set, BMCTrack-d outperforms strong baselines, achieving higher-order tracking accuracy gains of 9.11% and 1.03%.
arXiv Computer Vision
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ByteTraX: Enhancing the ByteTrack Architecture with Optimised Thresholding
ByteTraX is a lightweight enhancement to the ByteTrack multi‑object tracking architecture that introduces a single unified matching threshold and stricter track initiation criteria to reduce erroneous track reclassification and identity switches. The method yields consistent performance gains across several benchmarks—GMOT‑40, LC‑MOT, SportsMOT, TeamTrack, DAMUNT, and DeepSea‑MOT—while boosting processing speed by over 10%. Quantitatively, ByteTraX achieves more than a 40% drop in identity switches, with mean improvements of 3.6 in HOTA, 5.6 in IDF1, and 6.3 FPS.
Hugging Face Trending Papers
3d ago
ByteTraX: Enhancing the ByteTrack Architecture with Optimised Thresholding
The ByteTrack algorithm is a widely used and computationally efficient multi-object tracking architecture. Its core innovation lies in the combination of lenient bounding box associations with trackle...
Hugging Face Trending Papers
Sep 3
BMCTrack-d: Pig re-identification and tracking via back marks in challenging camera settings
BMCTrack-d is a novel tracking‑by‑detection system that uses unique back marks on pigs to enable reliable re‑identification and tracking in challenging side‑view camera settings. The approach combines a neural network back‑mark classifier with two post‑processing stages—temporal prediction consistency checks and deduplication—to improve identity reliability over time. On a demanding test set, BMCTrack‑d outperforms strong baselines (BoT‑SORT‑ReID and TrackTrack‑ReID) by 9.11% and 1.03% in higher‑order tracking accuracy, demonstrating the effectiveness of back‑mark‑based re‑identification for individual‑level pig monitoring.