  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.
