Gait recognition using inertial sensors

El estudio analítico sobre la forma de caminar de cada persona y de cómo puede utilizarse esto para identificar de manera inequívoca a cada individuo es lo que se tratará en el presente Trabajo de Fin de Grado (TFG). Se usarán un conjunto de sensores inerciales comúnes en el mercado, como puede ser el de un sistema IMU o un terminal móvil (Smartphone). El fin de este proyecto es el desarrollo de un algoritmo que pueda implementarse en múltiples plataformas o sistemas (con fines de seguridad), así como plantar las bases para futuros proyectos derivados de este, con fines como pueden ser la medicina, los deportes de élite u otros campos de investigación. Para la creación de este algoritmo se elegirá y trabajará en un sistema de desarrollo completo de modelos de Machine Learning y se experimentará con múltiples técnicas ya utilizadas en otros casos de uso o en el mismo. El objetivo final será el de hallar el modelo con mayor porcentaje de precisión en aciertos. A su vez, para la obtención de los datos, se ha desarrollado una aplicación móvil que recoge los mensurandos necesarios de los sensores y los sube a una base de datos para su posterior análisis

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

AttenGait: Gait recognition with attention and rich modalities

Embargado hasta 18/04/2026Current gait recognition systems employ different types of manual attention mechanisms, like horizontal cropping of the input data to guide the training process and extract useful gait signatures for people identification. Typically, these techniques are applied using silhouettes as input, which limits the learning capabilities of the models. Thus, due to the limited information provided by silhouettes, state-of-the-art gait recognition approaches must use very simple and manually designed mechanisms, in contrast to approaches proposed for other topics such as action recognition. To tackle this problem, we propose AttenGait, a novel model for gait recognition equipped with trainable attention mechanisms that automatically discover interesting areas of the input data. AttenGait can be used with any kind of informative modalities, such as optical flow, obtaining state-of-the-art results thanks to the richer information contained in those modalities. We evaluate AttenGait on two public datasets for gait recognition: CASIA-B and GREW; improving the previous state-of-the-art results on them, obtaining 95.8% and 70.7% average accuracy, respectivel