Ego-Object Discovery
This addresses the challenge of extracting meaningful information from lifelogging data for users and developers, though it appears incremental as it builds on existing object detection techniques.
The paper tackles the problem of discovering objects relevant to a person wearing a first-person camera from egocentric video/images, using a semi-supervised strategy that combines CNN appearance features, object refill, and SVM filtering to outperform state-of-the-art methods on new and existing datasets.
Lifelogging devices are spreading faster everyday. This growth can represent great benefits to develop methods for extraction of meaningful information about the user wearing the device and his/her environment. In this paper, we propose a semi-supervised strategy for easily discovering objects relevant to the person wearing a first-person camera. Given an egocentric video/images sequence acquired by the camera, our algorithm uses both the appearance extracted by means of a convolutional neural network and an object refill methodology that allows to discover objects even in case of small amount of object appearance in the collection of images. An SVM filtering strategy is applied to deal with the great part of the False Positive object candidates found by most of the state of the art object detectors. We validate our method on a new egocentric dataset of 4912 daily images acquired by 4 persons as well as on both PASCAL 2012 and MSRC datasets. We obtain for all of them results that largely outperform the state of the art approach. We make public both the EDUB dataset and the algorithm code.