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arXiv Computer Vision
Aug 28

Camera Calibration Using Inaccurate and Asynchronous Discrete GPS Trajectory from Drones

The paper tackles the problem of calibrating a stationary camera’s yaw, pitch, and roll using a drone’s GPS trajectory, which suffers from altitude bias, time offset, and discrete sampling. It formulates a parameter estimation problem that jointly estimates the GPS altitude bias, time offset, and camera orientation biases, and proposes a maximum likelihood estimator based on Iterated Least Squares to handle the asynchronous, discrete GPS data. Simulation results show the estimator achieves accuracy close to the Cramér–Rao Lower Bound, with a recommended drone trajectory yielding calibration errors within 14% of the measurement error standard deviation.

By R. Yang, Y. Bar-Shalom, H. A. J. Huang
roboticssafety
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arXiv Computer Vision
Sep 3

MuyBridge: Mobile Human Center-of-Mass Estimation from Monocular Video via Sparse Fusion

MuyBridge is an on-device system that estimates an athlete’s segmental center of mass (CoM) trajectory from a single phone camera video stream. It combines a compact 2D pose network with a distilled monocular depth network, fusing their outputs through anatomical and physical priors to produce metric CoM estimates without requiring 3D or task‑specific supervision. On the AthletePose3D dataset, MuyBridge achieves 33–41 mm vertical CoM error and 2.3–6.6 % absolute‑relative range error, delivering CoM estimates at 63 FPS on an iPhone 15 with asynchronous depth updates.

By Aidan Bradshaw, Marco Giordano, David Rode, Andreas Habersack, Elif Basokur, Annika Kruse, Markus Tilp, Michele Magno, Peter Wolf, Luca Benini, Christoph Leitner
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arXiv Computer Vision
Oct 12

What Egocentric Video Embeddings Encode and How to Combine Them with Inertial Sensors for Human Activity Recognition

The paper investigates what information is captured by egocentric video embeddings from models such as VideoMAEv2, CLIP, DINOv2, and VC-1, finding that the leading principal components largely reflect where the head‑mounted camera is looking. It also explores how to combine these video embeddings with body‑worn inertial sensor data for human activity recognition, showing that propagating inertial predictions over a video‑based graph can significantly improve classification performance (macro‑F1 from 0.648 to 0.852). The study demonstrates that video and inertial modalities play distinct roles—video identifies similar clips within a set, while inertial data labels the activities—yet fusing them directly in a single classifier or adding inertial edges to the graph does not yield better results.

By Kansuke Habano
rag
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