%0 Conference Proceedings
%T Batch-based activity recognition from egocentric photo-streams
%A Alejandro Cartas
%A Mariella Dimiccoli
%A Petia Radeva
%B 1st International workshop on Egocentric Perception, Interaction and Computing
%D 2017
%F Alejandro Cartas2017
%O MILAB; no menciona
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3023), last updated on Thu, 20 Jan 2022 15:38:59 +0100
%X Activity recognition from long unstructured egocentric photo-streams has several applications in assistive technology such as health monitoring and frailty detection, just to name a few. However, one of its main technical challenges is to deal with the low frame rate of wearable photo-cameras, which causes abrupt appearance changes between consecutive frames. In consequence, important discriminatory low-level features from motion such as optical flow cannot be estimated. In this paper, we present a batch-driven approach for training a deep learning architecture that strongly rely on Long short-term units to tackle this problem. We propose two different implementations of the same approach that process a photo-stream sequence using batches of fixed size with the goal of capturing the temporal evolution of high-level features. The main difference between these implementations is that one explicitly models consecutive batches by overlapping them. Experimental results over a public dataset acquired by three users demonstrate the validity of the proposed architectures to exploit the temporal evolution of convolutional features over time without relying on event boundaries.
%U http://arxiv.org/abs/1708.07889
%U http://refbase.cvc.uab.es/files/CDR2017.pdf