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

The VOT-RGBT 2019 Challenge
The Visual Object Tracking challenge for RGB and Thermal imagery. The link to...
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AMUSE
The automotive multi-sensor (AMUSE) dataset consists of inertial and other...
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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