Detección de ritmo cardiaco mediante vídeo

El objetivo de este trabajo fin de grado es diseñar y desarrollar un algoritmo que nos permita detectar el ritmo cardíaco mediante el análisis de secuencias de vídeo, tanto en secuencias naturales en color como en secuencias de máscaras de segmentación y de profundidad. El uso de estas últimas secuencias permite preservar la privacidad de la persona monitorizada y trabajar en condiciones de baja o nula iluminación. Se ha diseñado e implementado una nueva aproximación, basada en un algoritmo base fundamentado en el trabajo previo en el que nos apoyamos. Una vez realizado y validado el algoritmo base sobre secuencias naturales en colores, se ha diseñado y desarrollado la nueva aproximación: un algoritmo que trabaja sobre secuencias de color, al igual que el algoritmo base, y que además trabaja sobre secuencias de máscaras de segmentación y profundidad proporcionadas por la cámara Kinect. Se ha realizado un análisis del rendimiento de los distintos algoritmos en función de la distancia de la persona a la cámara con el fin de evaluar la viabilidad de un posible sistema combinado que utilice el nuevo algoritmo desarrollado. Para la validación del algoritmo se ha grabado un conjunto de secuencias de vídeo (dataset), compuesto por secuencias de color, máscaras de segmentación y profundidad, además de la grabación del ritmo cardíaco medido por un pulsímetro. Con este dataset se ha podido obtener un completo conjunto de resultados en las distintas situaciones bajo estudio.The objective of this Final Degree Thesis is to design and develop an algorithm that allows us to detect the heart rate by analyzing natural color, segmentation masks and depth video sequences. Using these last kind of sequences allows the preservation of the privacy of the monitored person and working in conditions of low or no lighting. A new approach has been designed and implemented, starting from a base algorithm based on previous work in which we rely. After implementing and validating the base algorithm on color natural sequences, the new approach has been designed and developed: an algorithm working on color sequences, as the base algorithm, but also on masks segmentation and depth sequences provided by the Kinect camera. We have analyzed the performance of di erent modalities depending on the distance of the camera to the person in order to assess the feasibility of a possible combined system using the various modalities supported by the proposed algorithm. To validate the algorithm, a dataset has been recorded, composed of sequences natural color, segmentation masks and depth sequences, in addition to recording of the heart rate measured by a heart rate monitor. Using this dataset a complete set of results in the di erent situations under study has been obteined

Long-Term tracking with target re-identification

El objetivo de este Trabajo Fin de Máster es mejorar el rendimiento del algoritmo de seguimiento de objetos PKLTF (Point-based Kanade Lucas Tomasi colour-Filter ). Para ello, se ha diseñado un algoritmo mejorado en función de las carencias que se han observado en el algoritmo base. Se han propuesto varias mejoras que se han ido implementando sobre el algoritmo base. Finalmente algunas de ellas se han incorporado al algoritmo propuesto SAPKLTF (Scale Adaptive Point-based Kanade Lucas Tomasi colour-Filter ). Estas mejoras implementadas permiten mejorar el rendimiento frente a los cambios de escala y mantener el rendimiento en tiempo real. Por último, el algoritmo de seguimiento de objetos propuesto se ha evaluado frente a una selección representativa de algoritmos de seguimiento de objetos del Estado del Arte. El nuevo algoritmo de seguimiento de objetos mejora el rendimiento del algoritmo base en la evaluación comparativa, asi como su competitividad frente a los del Estado del Arte.The objective of this Master Thesis is to improve the performance of an existing tracker, called PKLTF (Point-based Kanade Lucas Tomasi colour-Filter). A newly improved tracker is designed considering the problems that a ect the base tracker. Several improvements are tested, some of which are integrated into the proposed version SAPKLTF (Scale Adaptive Point-based Kanade Lucas Tomasi colour-Filter). These improvements allow to deal with scale changes and maintain the real-time performance. Finally, the proposed tracking algorithm is evaluated against a representative selection of trackers of the state-of-the-art. The new tracker improves the performance of the base tracker in the comparative evaluation, as well as this competitiveness against the ones for the State-of-the-Art

The Visual Object Tracking VOT2017 Challenge Results

The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website(1)

The Sixth Visual Object Tracking VOT2018 Challenge Results

The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).Funding agencies: Slovenian research agencySlovenian Research Agency - Slovenia [P2-0214, P2-0094, J2-8175]; Czech Science FoundationGrant Agency of the Czech Republic [GACR P103/12/G084]; WASP; VR (EMC2); SSF (SymbiCloud); SNIC; AIT Strategic Research Programme 2017 Visua</p