2015/04/07 by Marc Bolaños, Bolaños, Marc, Petia Radeva +1
Computer Science · #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Video Surveillance and Tracking Methods #Visual Attention and Saliency Detection #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.1504.01639
9 pages, 13 figures, Submitted to: Image and Vision Computing
openalex publication_date 2015/04/07 · arxiv created 2015/07/08 · arxiv updated 2015/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.