Towards dense object tracking in a 2D honeybee hive

From human crowds to cells in tissue, the detection and efficient tracking of multiple objects in dense configurations is an important and unsolved problem. In the past, limitations of image analysis have restricted studies of dense groups to tracking a single or subset of marked individuals, or to coarse-grained group-level dynamics, all of which yield incomplete information. Here, we combine convolutional neural networks (CNNs) with the model environment of a honeybee hive to automatically recognize all individuals in a dense group from raw image data. We create new, adapted individual labeling and use the segmentation architecture U-Net with a loss function dependent on both object identity and orientation. We additionally exploit temporal regularities of the video recording in a recurrent manner and achieve near human-level performance while reducing the network size by 94% compared to the original U-Net architecture. Given our novel application of CNNs, we generate extensive problem-specific image data in which labeled examples are produced through a custom interface with Amazon Mechanical Turk. This dataset contains over 375,000 labeled bee instances across 720 video frames at 2FPS, representing an extensive resource for the development and testing of tracking methods. We correctly detect 96% of individuals with a location error of ~ 7% of a typical body dimension, and orientation error of 12°, approximating the variability of human raters. Our results provide an important step towards efficient image-based dense object tracking by allowing for the accurate determination of object location and orientation across time-series image data efficiently within one network architecture.Funding for this work was provided by the OIST Graduate University to ASM and GS. Additional funding was provided by KAKENHI grants 16H06209 and 16KK0175 from the Japan Society for the Promotion of Science to AS

Histograma de orientación de gradientes aplicado al seguimiento múltiple de personas basado en video

El seguimiento múltiple de personas en escenas reales es un tema muy importante en el campo de Visión Computacional dada sus múltiples aplicaciones en áreas como en los sistemas de vigilancia, robótica, seguridad peatonal, marketing, etc., además de los retos inherentes que representa la identificación de personas en escenas reales como son la complejidad de la escena misma, la concurrencia de personas y la presencia de oclusiones dentro del video debido a dicha concurrencia. Existen diversas técnicas que abordan el problema de la segmentación de imágenes y en particular la identificación de personas, desde diversas perspectivas; por su parte el presente trabajo tiene por finalidad desarrollar una propuesta basada en Histograma de Orientación de Gradientes (HOG) para el seguimiento múltiple de personas basado en video. El procedimiento propuesto se descompone en las siguientes etapas: Procesamiento de Video, este proceso consiste en la captura de los frames que componen la secuencia de video, para este propósito se usa la librería OpenCV de tal manera que se pueda capturar la secuencia desde cualquier fuente; la siguiente etapa es la Clasificación de Candidatos, esta etapa se agrupa el proceso de descripción de nuestro objeto, que para el caso de este trabajo son personas y la selección de los candidatos, para esto se hace uso de la implementación del algoritmo de HOG; por último la etapa final es el Seguimiento y Asociación, mediante el uso del algoritmo de Kalman Filter, permite determinar las asociaciones de las secuencias de objetos previamente detectados. La propuesta se aplicó sobre tres conjuntos de datos, tales son: TownCentre (960x540px), TownCentre (1920x1080px) y PETS 2009, obteniéndose los resultados para precisión: 94.47%, 90.63% y 97.30% respectivamente. Los resultados obtenidos durante las experimentaciones validan la propuesta del modelo haciendo de esta una herramienta que puede encontrar múltiples campos de aplicación, además de ser una propuesta innovadora a nivel nacional dentro del campo de Vision Computacional.Tesi

Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking

This work presents the current state-of-the-art in techniques for tracking a number of objects moving in a coordinated and interacting fashion. Groups are structured objects characterized with particular motion patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport players, convoy of cars) or of hundreds or thousands of components such as crowds of people. The group object tracking is closely linked with extended object tracking but at the same time has particular features which differentiate it from extended objects. Extended objects, such as in maritime surveillance, are characterized by their kinematic states and their size or volume. Both group and extended objects give rise to a varying number of measurements and require trajectory maintenance. An emphasis is given here to sequential Monte Carlo (SMC) methods and their variants. Methods for small groups and for large groups are presented, including Markov Chain Monte Carlo (MCMC) methods, the random matrices approach and Random Finite Set Statistics methods. Efficient real-time implementations are discussed which are able to deal with the high dimensionality and provide high accuracy. Future trends and avenues are traced. © 2013 Elsevier Inc. All rights reserved

Emploi d'un réseau de détecteurs Wi-Fi pour l'étude et la modélisation du comportement de piétons

RÉSUMÉ La densification des villes est la cause d’un besoin grandissant de comprendre les déplacements des gens qui y habitent. La prédiction et la gestion de la circulation automobile sont des domaines bien développés, plus que ces mêmes domaines au sujet des piétons. De plus en plus, les villes réorientent leurs objectifs de développement vers les piétons, ou les cyclistes au détriment des automobilistes. Cela mène à la création de zones réservées aux piétons, qui deviennent de plus en plus des centres commerciaux et culturels importants au développement de la ville. Ce texte porte sur l’analyse d’une nouvelle technologie qui permet de faire la collecte de données de telles zones, et des résultats qui serviront à l’amélioration de zones piétonnes existantes et à la conception de nouvelles.----------ABSTRACT Urban densification is the cause of a growing need to understand the travel behaviours of people living in cities. Prediction and management of automobile traffic are well-developed fields, moreso than those same fields about pedestrians. More and more, cities are reorienting their develpment objectives to gear them towards pedestrians or cyclists at the expense of cars. This has lead to the creation of numerous pedestrian-only zones, that have become important commercial and cultural centers of city development. This document relates the analysis of a new technology that allows data collection of these areas, and the results will benefit the development of existing and new pedestrian-only areas

Identificação de Pedestres por meio de mapas de densidade construídos com ASIFT e Fluxo Óptico

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