CAD-based Learning for Egocentric Object Detection in Industrial Context

Julia Cohen, Carlos Crispim-Junior, Céline Grange-Faivre, Laure Tougne

Industries nowadays have an increasing need of real-time and accurate vision-based algorithms. Although the performance of object detection methods improved a lot thanks to massive public datasets, instance detection in industrial context must be approached differently, since annotated images are usually unavailable or rare. In addition, when the video stream comes from a head-mounted camera, we observe a lot of movements and blurred frames altering the image content. For this purpose, we propose a framework to generate a dataset of egocentric synthetic images using only CAD models of the objects of interest. To evaluate different strategies exploiting synthetic and real images, we train a Convolutional Neural Network (CNN) for the task of object detection in egocentric images. Results show that training a CNN on synthetic images that reproduce the characteristics of egocentric vision may perform as well as training on a set of real images, reducing, if not removing, the need to manually annotate a large quantity of images to achieve an accurate performance.

Link: http://insticc.org/node/TechnicalProgram/visigrapp/presentationDetails/89755