Person Re-Identification by Deep Joint Learning of Multi-Loss Classification

Existing person re-identification (re-id) methods rely mostly on either localised or global feature representation alone. This ignores their joint benefit and mutual complementary effects. In this work, we show the advantages of jointly learning local and global features in a Convolutional Neural Network (CNN) by aiming to discover correlated local and global features in different context. Specifically, we formulate a method for joint learning of local and global feature selection losses designed to optimise person re-id when using only generic matching metrics such as the L2 distance. We design a novel CNN architecture for Jointly Learning Multi-Loss (JLML) of local and global discriminative feature optimisation subject concurrently to the same re-id labelled information. Extensive comparative evaluations demonstrate the advantages of this new JLML model for person re-id over a wide range of state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03, Market-1501).Comment: Accepted by IJCAI 201

Design, implementation and evaluation of automated surveillance systems

El reconocimiento de patrones ha conseguido un nivel de complejidad que nos permite reconocer diferente tipo de eventos, incluso peligros, y actuar en concordancia para minimizar el impacto de una situación complicada y abordarla de la mejor manera posible. Sin embargo, creemos que todavía se puede llegar a alcanzar aplicaciones más eficientes con algoritmos más precisos. Nuestra aplicación quiere probar a incluir el nuevo paradigma de la programación, las redes neuronales. Nuestra idea en principio fue explorar la alternativa que las nuevas redes neuronales convolucionales aportaban, en donde se podía ver en vídeos de ejemplos la alta tasa de detección e identificación que, por ejemplo, YOLOv2 podría mostrar. Después de comparar las características, vimos que YOLOv3 ofrecía un buen balance entre precisión y rapidez como comentaremos más adelante. Debido a la tasa de baja detecciones, haremos uso de los filtros de Kalman para ayudarnos a la hora de hacer reidentificación de personas y objetos. En este proyecto, haremos un estudio además de las alternativas de videovigilancia con las que cuentan empresas del sector y veremos que clase de productos ofrecen y, por otro lado, observaremos cuales son los trabajos de los grupos de investigadores de otras universidades que más similitudes tienen con nuestro objetivo. Dedicaremos, por lo tanto, el uso de esta red neuronal para detectar eventos como el abandono de mochilas y para mostrar la densidad de tránsito en localizaciones concretas, así como utilizaremos una metodología más tradicional, el flujo óptico, para detectar actuaciones anormales en una multitud.Automatic surveillance system is getting more and more sophisticated with the increasing calculation power that computers are reaching. The aim of this project is to take advantage of these tools and with the new classification and detection technology brought by neural networks, develop a surveillance application that can recognize certain behaviours (which are the detection of lost backpacks and suitcases, detection of abnormal crowd activity and heatmap of density occupation). To develop this program, python has been the selected programming language used, where YOLO and OpenCV form the spine of this project. After testing the code, it has been proved that due to the constrains of the detection for small objects, the project does not perform as it should for real development, but still it shows potential for the detection of lost backpacks in certain videos from the GBA dataset [1] and PETS2006 dataset [2]. The abnormal activity detection for crowds is made with a simple algorithm that seems to perform well, detecting the anomalies in all the testing dataset used, generated by the University of Minnesota [3]. Finally, the heatmap can display correctly the projection of people on the ground for five second, just as intended. The objective of this software is to be part of the core of what could be a future application with more modules that will be able to perform full automated surveillance tasks and gather useful information data, and these advances and future proposal will be explained in this memory.Máster Universitario en Ingeniería Industrial (M141