Automotive Multi-sensor (AMUSE) Dataset

The automotive multi-sensor (AMUSE) dataset consists of inertial and other complementary sensor data combined with monocular, omnidirectional, high frame rate visual data taken in real traffic scenes during multiple test drives. Omnidirectional visual data for full surround sensing; include winter conditions with snow (Published in 2013)

Data and Resources

Additional Info

Field Value
Maintainer Linköping University
Number of Instances 14
Package Description VOT-RGBT 2019 AMUSE GoPro IMU Dataset GoPro Gyro Dataset Kinect v2 Dataset LTIR Object Pose Estimation Database PASSTA Datasets Rolling Shutter Rectification Dataset Rolling Shutter Bundle Adjustment Dataset Swedish Leaf Dataset Traffic Signs Dataset TST-Priv Video Stacking Dataset
Dataset has missing values False