Audio-Visual Dog Identification and Tracking in Real-World Videos
Read the original on arXiv Machine Learning →The paper introduces a multimodal dog‑fingerprinting framework that combines visual identification, multiple‑object tracking, and canine vocal fingerprinting to associate bark events with individual dogs in real‑world videos. It synchronizes bark events with tracked dogs and updates persistent profiles using both visual appearance and vocal characteristics, allowing identification even when dogs are occluded, overlapping vocalizations occur, or dogs bark off‑screen. The authors also present a benchmark of 46 real‑world videos with 213 manually annotated bark events, reporting a 61.83% localization accuracy for visible dogs (IoU ≥ 0.30) and a 91.33% F1 score for detecting visible versus off‑screen barking.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.