Batch-Based Activity Recognition From Egocentric Photo-Streams

Alejandro Cartas, Mariella Dimiccoli, Petia Radeva; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2347-2354


Activity recognition from unstructured egocentric photo-streams has several applications in assistive technology such as health monitoring. However, one of its main challenges is to deal with the low frame rate of wearable photo-cameras, which causes abrupt appearance changes between consecutive frames making motion estimation unfeasible. 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. 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.

Related Material

author = {Cartas, Alejandro and Dimiccoli, Mariella and Radeva, Petia},
title = {Batch-Based Activity Recognition From Egocentric Photo-Streams},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {Oct},
year = {2017}