A weakly-supervised approach for discovering common objects in airport video surveillance footage

Object detection in video is a relevant task in computer vision. Standard and current detectors are typically trained in a strongly supervised way, what requires a huge amount of labelled data. In contrast, in this paper we focus on object discovery in video sequences by using sets of unlabelled data. Thus, we present an approach based on the use of two region proposal algorithms (a pretrained Region Proposal Network and an Optical Flow Proposal) to produce regions of interest that will be grouped using a clustering algorithm. Therefore, our system does not require the collaboration of a human except for assigning human understandable labels to the discovered clusters. We evaluate our approach in a set of videos recorded at the outdoor area of an airport where the aeroplanes park to load passengers and luggage (apron area). Our experimental results suggest that the use of an unsupervised approach is valid for automatic object discovery in video sequences, obtaining a CorLoc of 86.8 and a mAP of 0.374 compared to a CorLoc of 70.4 and mAP of 0.683 achieved by a supervised Faster R-CNN trained and tested on the same dataset.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Gait recognition and fall detection with inertial sensors

In contrast to visual information that is recorded by cameras placed somewhere, inertial information can be obtained from mobile phones that are commonly used in daily life. We present in this talk a general deep learning approach for gait and soft biometrics (age and gender) recognition. Moreover, we also study the use of gait information to detect actions during walking, specifically, fall detection. We perform a thorough experimental evaluation of the proposed approach on different datasets: OU-ISIR Biometric Database, DFNAPAS, SisFall, UniMiB-SHAR and ASLH. The experimental results show that inertial information can be used for gait recognition and fall detection with state-of-the-art results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Gait recognition applying Incremental learning

when new knowledge needs to be included in a classifier, the model is retrained from scratch using a huge training set that contains all available information of both old and new knowledge. However, in this talk, we present a way to include new information in a previously trained model without training from scratch and using a small subset of old data. We perform a thorough experimental evaluation of the proposed approach on two image classification datasets: CIFAR-100 and ImageNet. The experiment results show that it is possible to include new knowledge in a model without forgetting the previous one, although, the performance is still lower than training from scratch with the complete training set.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

UCO physical rehabilitation: new dataset and study of human pose estimation methods on physical rehabilitation exercises

Physical rehabilitation plays a crucial role in restoring motor function following injuries or surgeries. However, the challenge of overcrowded waiting lists often hampers doctors’ ability to monitor patients’ recovery progress in person. Deep Learning methods offer a solution by enabling doctors to optimize their time with each patient and distinguish between those requiring specific attention and those making positive progress. Doctors use the flexion angle of limbs as a cue to assess a patient’s mobility level during rehabilitation. From a Computer Vision perspective, this task can be framed as automatically estimating the pose of the target body limbs in an image. The objectives of this study can be summarized as follows: (i) evaluating and comparing multiple pose estimation methods; (ii) analyzing how the subject’s position and camera viewpoint impact the estimation; and (iii) determining whether 3D estimation methods are necessary or if 2D estimation suffices for this purpose. To conduct this technical study, and due to the limited availability of public datasets related to physical rehabilitation exercises, we introduced a new dataset featuring 27 individuals performing eight diverse physical rehabilitation exercises focusing on various limbs and body positions. Each exercise was recorded using five RGB cameras capturing different viewpoints of the person. An infrared tracking system named OptiTrack was utilized to establish the ground truth positions of the joints in the limbs under study. The results, supported by statistical tests, show that not all state-of-the-art pose estimators perform equally in the presented situations (e.g., patient lying on the stretcher vs. standing). Statistical differences exist between camera viewpoints, with the frontal view being the most convenient. Additionally, the study concludes that 2D pose estimators are adequate for estimating joint angles given the selected camera viewpoints

RBM-based Silhouette Encoding for Human Action Modelling

Abstract—In this paper we evaluate the use of Restricted Bolzmann Machines (RBM) in the context of learning and recognizing human actions. The features used as basis are binary silhouettes of persons. We test the proposed approach on two datasets of human actions where binary silhouettes are available: ViHASi (synthetic data) and Weizmann (real data). In addition, on Weizmann dataset, we combine features based on optical flow with the associated binary silhouettes. The results show that thanks to the use of RBM-based models, very informative and shorter feature vectors can be obtained for the classification tasks, improving the classification performance. Keywords-Restricted Boltzmann Machines; binary silhouettes; human actions

MODASC: ASIC for mobile data acquisition systems using satellite communications

MODASC is an ASIC that performs wide area mobile data acquisition using satellite communication to provide global coverage. This circuit provides two operation modes: an autonomous mode, that periodically establishes connection with the control center, and a slave mode, working as a peripheral connected to a general purpose micro controller. This experiment has been realized under FUSE special action in collaboration with SAINSEL

¿Se asocia el consumo de refrescos azucarados con la composición corporal? Estudio transversal en adolescentes españoles

Objectives: Published data about the association between the consumption of sweetened soft-drinks (SSD) and obesity in childhood are controversial and still inconsistent. In addition, data are lacking in the Spanish population. The purpose of this study was therefore, to explore the cross-sectional association between body composition- related parameters and SSD consumption in Spanish adolescents. Subjects and methods: A representative sample of 1,523 adolescents (768 boys and 755 girls), with complete dietary data as well as anthropometric measurements, were included in this study. Weight, height, waist circumferences, and 6 skinfolds were measured, and BMI and percentage body fat were calculated. From a 24h dietary recall the subjects were grouped in 3 groups according to their SSD consumption: 1) Non-consumers (0 g of SSD consumption); 2) Moderate consumption (< 336 g/day of SSD, equivalent to the average SSD portion size); and 3) High consumption (> 336 g/day of SSD). Results: 67% males and 75% females did not consume any SSD the day before the dietary recall interview. Males consumed more SSD than females. Regarding the association between SSD consumption and measures of obesity, no difference was observed between the three groups of SSD consumption in any of the anthropometric measurement, BMI or body fat. Conclusion: As no association was present between SSD consumption and obesity in our cross-sectional study we suggest that dietary patterns and habits as well as lifestyle factors such as physical activity should be present when examining cross-sectional or longitudinal relationships with obesity. Multidisciplinary intervention studies are crucial when trying to develop solutions against the increasing obesity epidemic.Objetivos: Los datos publicados sobre la asociación entre el consumo de refrescos azucarados (SSD) y la obesidad en la infancia son controvertidos y todavía inconsistentes. Además, estos datos son muy escasos en la población española. Por ello, el propósito de este estudio ha sido estudiar la asociación entre los parámetros relacionados con la composición corporal y el consumo de SSD en adolescentes españoles. Sujetos y métodos: Se ha realizado el estudio en una muestra de 1.523 adolescentes (768 chicos y 755 chicas) que tenían cumplimentados los datos dietéticos y los parámetros antropométricos (peso, altura, circunferencias de cintura, y 6 pliegues). Se calculó el IMC y el porcentaje de grasa corporal. La dieta ha sido calculada a partir de un recordatorio de 24h. Los sujetos fueron divididos en grupos dependiendo de la cantidad de SSD que consumían: 1) No consumidores (0 g de consumo SSD); 2) Consumo moderado (< 336 g/día de SSD, equivalente a una bebida al día de SSD); y 3) Consumo alto (> 336 g/día de SSD). Resultados: El 67% de los varones y el 75% de las mujeres indican no consumir este tipo de bebidas el día anterior a la encuesta. Los varones en general consumieron más SSD que las mujeres. En cuanto a la asociación entre consumo SSD y medidas antropométricas y de composición corporal, no se encontraron diferencias significativas entre los tres grupos de estudio en los parámetros antropométricos, IMC o grasa corporal. Conclusión: Dado que no se ha encontrado en este estudio ninguna asociación entre el consumo de SSD y la obesidad, sugerimos que los patrones y hábitos dietarios así como los factores del estilo de vida, y la actividad física, deberían tenerse en cuenta al examinar las relaciones transversales o longitudinales con la obesidad, y que los estudios de intervención multidisciplinar son cruciales cuando se trata de desarrollar soluciones contra el incremento de una epidemia como la obesidad.The AVENA study was supported by Spanish Ministry of Health (00/0015) and by grants from the Spanish Higher Sports Council (05/UPB32/01, 09/UPB31/03 and 13/UPB20/04), the Spanish Ministry of Education (AP2003-2128 and AP2004-2745), Coca-Cola, Panrico SA, Madaus SA and Procter & Gamble SA