SVIRO: Synthetic Vehicle Interior Rear Seat Occupancy Dataset and Benchmark

Autor:	Da Cruz, Steve Dias, Wasenmüller, Oliver, Beise, Hans-Peter, Stifter, Thomas, Stricker, Didier
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	We release SVIRO, a synthetic dataset for sceneries in the passenger compartment of ten different vehicles, in order to analyze machine learning-based approaches for their generalization capacities and reliability when trained on a limited number of variations (e.g. identical backgrounds and textures, few instances per class). This is in contrast to the intrinsically high variability of common benchmark datasets, which focus on improving the state-of-the-art of general tasks. Our dataset contains bounding boxes for object detection, instance segmentation masks, keypoints for pose estimation and depth images for each synthetic scenery as well as images for each individual seat for classification. The advantage of our use-case is twofold: The proximity to a realistic application to benchmark new approaches under novel circumstances while reducing the complexity to a more tractable environment, such that applications and theoretical questions can be tested on a more challenging dataset as toy problems. The data and evaluation server are available under https://sviro.kl.dfki.de. Comment: This paper is accepted at IEEE Winter Conference on Applications of Computer Vision (WACV), 2020. Supplementary material is available under https://sviro.kl.dfki.de/downloads/papers/wacv_supplementary.pdf
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2001.03483 Zobrazit plný text záznamu View this record from Arxiv