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Action spotting in soccer videos is the task of identifying the specific time when a certain key action of the game occurs. Lately, it has received a large amount of attention and powerful methods have been introduced. Action spotting involves understanding the dynamics of the game, the complexity of events, and the variation of video sequences. Most approaches have focused on the latter, given that their models exploit the global visual features of the sequences. In this work, we focus on the former by (a) identifying and representing the players, referees, and goalkeepers as nodes in a graph, and by (b) modeling their temporal interactions as sequences of graphs. For the player identification, or player classification task, we obtain an accuracy of 97.72% in our annotated benchmark. For the action spotting task, our method obtains an overall performance of 57.83% average-mAP by combining it with other audiovisual modalities. This performance surpasses similar graph-based methods and has competitive results with heavy computing methods. Code and data are available at https://github.com/IPCV/soccer_action_spotting., Comment: Accepted at the 5th International ACM Workshop on Multimedia Content Analysis in Sports (MMSports 2022)

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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.

Activity recognition from wearable photo-cameras is crucial for lifestyle characterization and health monitoring. However, to enable its wide-spreading use in real-world applications, a high level of generalization needs to be ensured on unseen users. Currently, state-of-the-art methods have been tested only on relatively small datasets consisting of data collected by a few users that are partially seen during training. In this paper, we built a new egocentric dataset acquired by 15 people through a wearable photo-camera and used it to test the generalization capabilities of several state-of-the-art methods for egocentric activity recognition on unseen users and daily image sequences. In addition, we propose several variants to state-of-the-art deep learning architectures, and we show that it is possible to achieve 79.87% accuracy on users unseen during training. Furthermore, to show that the proposed dataset and approach can be useful in real-world applications, where data can be acquired by different wearable cameras and labeled data are scarcely available, we employed a domain adaptation strategy on two egocentric activity recognition benchmark datasets. These experiments show that the model learned with our dataset, can easily be transferred to other domains with a very small amount of labeled data. Taken together, those results show that activity recognition from wearable photo-cameras is mature enough to be tested in real-world applications., This work was supported in part by the TIN2018-095232-B-C21, in part by the SGR-2017 1742, in part by the Nestore ID: 769643, in part by the Validithi EIT Health Program, in part by the CERCA Programme/Generalitat de Catalunya, in part by the Spanish Ministry of Economy and Competitiveness, and in part by the European Regional Development Fund (MINECO/ERDF, EU) through the program Ramon y Cajal. The work of Alejandro Cartas was supported by a doctoral fellowship from the Mexican Council of Science and Technology (CONACYT) under Grant 366596.

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.

Recently, there has been a growing interest in analyzing human daily activities from data collected by wearable cameras. Since the hands are involved in a vast set of daily tasks, detecting hands in egocentric images is an important step towards the recognition of a variety of egocentric actions. However, besides extreme illumination changes in egocentric images, hand detection is not a trivial task because of the intrinsic large variability of hand appearance. We propose a hand detector that exploits skin modeling for fast hand proposal generation and Convolutional Neural Networks for hand recognition. We tested our method on UNIGE-HANDS dataset and we showed that the proposed approach achieves competitive hand detection results.

One of the most interesting goals in engineering and the sciences is the mathematical representation of physical, social and other kind of complex phenomena. This goal has been attempted and, lately, achieved with different machine learning (ML) tools. ML owes much of its present appeal to the fact that it allows to model complex phenomena without the explicit definition of the form of the model. Neural networks and support vector machines exemplify such methods. However, in most of the cases, these methods yield “black box” models, i.e. input and output correspond to the phenomena under scrutiny but it is very difficult (or outright impossible) to discern the interrelation of the input variables involved. In this paper we address this problem with the explicit aim of targeting on models which are closed in nature, i.e. the aforementioned relation between variables is explicit. In order to do this, in general, the only assumption regarding the data is that they be approximately continuous. In such cases it is possible to represent the system with polynomial expressions. To be able to do so one must define the number of monomials, the degree of every variable in every monomial and the coefficients associated. We model sparse data systems with an algorithm minimizing the min-max norm. From mathematical and experimental evidence we are able to set a bound on the number of terms and degrees of the approximating polynomials. Thereafter, a genetic algorithm (GA) identifies the coefficients which correspond to the terms and degrees defined as above.

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.

Recognizing Activities of Daily Living (ADLs) has a large number of health applications, such as characterize lifestyle for habit improvement, nursing and rehabilitation services. Wearable cameras can daily gather large amounts of image data that provide rich visual information about ADLs than using other wearable sensors. In this paper, we explore the classification of ADLs from images captured by low temporal resolution wearable camera (2 fpm) by using a Convolutional Neural Networks (CNN) approach. We show that the classification accuracy of a CNN largely improves when its output is combined, through a random decision forest, with contextual information from a fully connected layer. The proposed method was tested on a subset of the NTCIR-12 egocentric dataset, consisting of 18,674 images and achieved an overall accuracy of 86% activity recognition on 21 classes.

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

We discuss an algorithm which allows us to find the algebraic expression of a dependent variable as a function of an arbitrary number of independent ones where data is arbitrary, i.e. it may have arisen from experimental data. The possibility of such approximation is proved starting from the Universal Approximation Theorem (UAT). As opposed to the neural network (NN) approach to which it is frequently associated, the relationship between the independent variables is explicit, thus resolving the “black box” characteristics of NNs. It implies the use of a nonlinear function (called the activation function) such as the logistic 1/(1+e− x ). Thus, any function is expressible as a combination of a set of logistics. We show that a close polynomial approximation of logistic is possible by using only a constant and monomials of odd degree. Hence, an upper bound (D) on the degree of the polynomial may be found. Furthermore, we may calculate the form of the model resulting from D. We discuss how to determine the best such set by using a genetic algorithm leading to the best L∞- L2 approximation. It allows us to find the best approximation polynomial given a selected fixed number of coefficients. It then finds the best combination of coefficients and their values. We present some experimental results.