Multi-layer attention for person re-identification

Person re-identification has been a significant application in the field of video surveillance analysis, yet it remains a challenging work to recognize the person of interest across disjoint cameras of different viewpoints. The factors affecting the identification results include the variation in background, different illumination conditions and the changes of human body poses. Existing person re-identification methods mainly focus on the feature extraction of the whole frame and metric learning functions. However, most of those algorithms treat different areas without distinction. It is worth emphasizing that different local regions make different contributions to image representaion, which exactly conforms to the attention mechanism. In this paper, we introduce a novel attention network which explores spatial attention in a convolutional neural network. Our algorithm learns the visual attention in multi-layer feature maps. The proposed model not only pays attention to the spatial probabilities of local regions, but also takes the features in different levels into consideration. We evaluate this multi-layer spatial attention model on three benchmark person re-identification datasets: Market-1501, CUHK03, and DukeMTMC-reID. The experiment results validate the advances of our adopted network by comparing with state-of-the-art baselines

Vermont Bicycle and Pedestrian Counting Program

Traffic counts are used extensively in transportation system management, planning, policy and research. Counts help us better understand spatial relationships and temporal trends in travel activity. In spite of the growing recognition of the importance of non-motorized travel, tracking of bicyclist and pedestrian travel behavior with counts lags behind comparable efforts focused on motorized travel. Count data helps agencies to better understand the non-motorized transportation activity in their jurisdictions by designing and prescribing: • data collection locations to count non-motorized transportation users; • methods appropriate for counting at each location; • data processing and management structures to assemble and quality assure data; and, • web portals to disseminate the information to the public and other stakeholders. In Vermont, non-motorized traffic counts are collected by the UVM TRC, VTrans, and several of the state’s regional planning commissions. RPCs collect counts in support of local initiatives and at the request of VTrans. The VTrans Traffic Research Unit has also collected a series of manual counts and the Agency recently purchased data from Strava, Inc., which includes data on routes used by cyclists who used the Strava app between 2014 and 2016 in Vermont. Strava’s mobile app and its desktop website interface allow athletes to track, analyze, plan, and share their training rides and runs. The Strava Metro product anonymizes and aggregates all of the cycling (and running) data recorded by Strava members for the given time frame aggregated onto a GIS of the street network. The variety of collection efforts creates a diverse set of statewide count data, but it makes compilation of a single state-wide archive challenging. The goals of this project were to create a bicycle and pedestrian count database for the state of Vermont, communicate the state of non motorized travel statewide, and make recommendations for future data collection and management

Re-identificación de personas utilizando únicamente información de profundidad

La finalidad de este trabajo es el diseño, implementación y evaluación de un sistema de re-identificación de personas a partir de imágenes de profundidad obtenidas por un sensor de tiempo de vuelo (ToF) ubicado en posición cenital. Este trabajo parte del detector desarrollado por el grupo GEINTRA y añade nuevas funcionalidades al sistema: ejecución en tiempo real y re-identificación de personas. La detección y la re-identificación se realizan con un clasificador basado en la técnica de Análisis de Componentes Principales (PCA). Para la validación del sistema se ha utilizado una base de datos de imágenes de profundidad cumpliendo con éxito los objetivos propuestos.The main objective of this work is the design, implementation and evaluation of a system capable of re-identifying people using only depth images obtained by a Time of Flight (ToF) sensor placed in zenithal position. This work starts uses the detector develoved by the GEINTRA group and adds new funtionalities to the system: real-time ejecution and people re-identification. The detection and reidentification of people are done by a clasificator based on the Principal Component Analysis (PCA) technique. The validation of the systeam has been done using a data base of depth images, achieving the proposed goals.Grado en Ingeniería en Tecnologías de Telecomunicació