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Trabajo presentado en el ICPR International Workshops and Challenges, celebrado de forma virtual del 10 al 15 de enero de 2021, The recognition of human activities captured by a wearable photo-camera is especially suited for understanding the behavior of a person. However, it has received comparatively little attention with respect to activity recognition from fixed cameras.In this work, we propose to use segmented events from photo-streams as temporal boundaries to improve the performance of activity recognition. Furthermore, we robustly measure its effectiveness when images of the evaluated person have been seen during training, and when the person is completely unknown during testing. Experimental results show that leveraging temporal boundary information on pictures of seen people improves all classification metrics, particularly it improves the classification accuracy up to 85.73%., Lecture Notes in Computer Science 12663, This work was partially funded by projects RTI2018-095232-B-C2, SGR 1742, CERCA, Nestore Horizon2020 SC1-PM-15-2017 (n 769643), Validithi EIT Health Program, and the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (MINECO/ERDF, EU) through the program Ramon y Cajal, the national Spanish project PID2019-110977GA-I00 and the Spanish national network RED2018-102511-T. A. Cartas supported by a doctoral fellowship from the Mexican Council of Science and Technology (CONACYT) (grant-no. 366596). The authors acknowledge the support of NVIDIA Corporation for hardware donation.

Trabajo presentado en la International Conference on Computer Vision Workshop (ICCVW), celebrada en Seúl (Corea del Sur), los días 27 y 28 de octubre de 2019, Our interaction with the world is an inherently multi-modal experience. However, the understanding of human-to-object interactions has historically been addressed focusing on a single modality. In particular, a limited number of works have considered to integrate the visual and audio modalities for this purpose. In this work, we propose a multimodal approach for egocentric action recognition in a kitchen environment that relies on audio and visual information. Our model combines a sparse temporal sampling strategy with a late fusion of audio, spatial,and temporal streams. Experimental results on the EPIC-Kitchens dataset show that multimodal integration leads to better performance than unimodal approaches. In particular, we achieved a5.18%improvement over the state of the art on verb classification., This work was partially funded by TIN2018-095232-B-C21, 2017 SGR 1742, Nestore, Validithi, 20141510 (La MaratoTV3) and CERCA Programme/Generalitat de Catalunya. A.C. is supported by a doctoral fellowship from the Mexican Council of Science and Technology (CONACYT) (grant-no. 366596). We acknowledge the support of NVIDIA Corporation with the donation of Titan Xp GPUs

Activity recognition is a very active area of research in computer vision. During the last decade, several efforts have focused on recognizing human activities in the wild from unedited videos posted in Youtube or captured by video-surveillance cameras. More recently, following the success of wearable cameras, a lot of interest has shifted towards the analysis of activities captured from a first-person point of view. In this chapter, we focus on the problem of activity recognition from visual lifelogs, that is, a set of images captured at regular intervals from a wearable photo-camera, as opposite to egocentric video shots. After describing the problem and the challenges it involves, we provide a detailed picture of the state of the art, also supported by an experimental survey of existing methods. Finally, we highlight limitations of the current approaches for real world applications and, in view of our analysis, we outline future challenges and research lines.

Wearable cameras can gather large a\-mounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a Late Fusion Ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85\%, outperforming state of the art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average.

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., Comment: 8 pages, 7 figures, 1 table. To appear at the ICCV 2017 workshop on Egocentric Perception, Interaction and Computing