Predicting Future Instance Segmentation by Forecasting Convolutional Features

Anticipating future events is an important prerequisite towards intelligent behavior. Video forecasting has been studied as a proxy task towards this goal. Recent work has shown that to predict semantic segmentation of future frames, forecasting at the semantic level is more effective than forecasting RGB frames and then segmenting these. In this paper we consider the more challenging problem of future instance segmentation, which additionally segments out individual objects. To deal with a varying number of output labels per image, we develop a predictive model in the space of fixed-sized convolutional features of the Mask R-CNN instance segmentation model. We apply the "detection head'" of Mask R-CNN on the predicted features to produce the instance segmentation of future frames. Experiments show that this approach significantly improves over strong baselines based on optical flow and repurposed instance segmentation architectures

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

Heating energy consumption forecasting based on machine learning

The author’s aim in this thesis project was to develop a machine learning model, which could create short-term forecasts regarding heating energy consumption of a building. Even short-term energy consumption forecasts can have a major impact on building automation and energy distribution systems. Possible application spheres include smart grid development and simpler maintenance. A feed forward artificial neural network was designed as a result of examination and testing of different models in order to get the most accurate predictions possible. To create an effective neural network various loss and activation functions as well as optimizers were reviewed. To obtain better results some preprocessing techniques were applied to filter corrupted and unreliable data. The designed model was successfully trained to perform forecasting on data from the same distribution as the training data

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Time series forecasting using SARIMA and SANN models

Information and communications technology has evolved to the point of being present in most things in our daily lives. Even the simplest object that everyone has in their home is getting smarter, like toothbrushes, cars, phones and so on. All that devices are connected to the Internet to make our life easier. The question is, how is all that amount of data processed? Here is when Artificial Intelligence appears. AI is the part of ICT dedicated to the development of algorithms that allows a machine to make intelligent decisions or, at least, behave as if it has a human-like intelligence. The use of AI is present in many sectors such finance, health, transport, or even agriculture. Machine Learning is a branch of AI based on the idea that computer systems can learn on their own from data. Data science has implemented Machine Learning algorithm such as the Artificial Neural Network to work with Statistics and Linear Regression for data processing. An ANN is the piece of a computing system designed to simulate the way the human brain analyses and processes information. It is the foundation of AI and solves problems that would prove impossible or difficult by human or statistical standards. But, is this resource always the best solution? This paper is about a comparison between Seasonal Artificial Neural Network with classic models as Seasonal Autoregressive Integrated Moving Average for rainfall forecasting. The project started by doing an introduction to Deep Learning and Machine Learning. Afterwards, the process of obtaining an adequate amount of data to create a proper dataset began. To do that, we used data from of some pluviometers distributed over the Hauts-de-Seineterritory from French government. With data from 2009 to 2020 of 19 sensors, the dataset was used to experiment with different algorithms and different configurations to obtain different predictions. The forecasting performance of SARIMA model and that of SANN were compared with four forecast performance measures: - Mean Forecast Error, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. Not only will the accuracy of the model be taken into account, but also the runtime and implementation requirements will be used as a benchmark. Finally, all models were tested in the same work environment and a conclusion was reached thanks to the results obtained from different reference points.Las Tecnologías de la información y la comunicación han evolucionado hasta el punto de estar presentes en la mayoría de las cosas de nuestra vida diaria. Incluso el objeto más simple que todos tienen en su hogar se está volviendo más inteligente, como cepillos de dientes, automóviles, teléfonos, etc. Todos esos dispositivos están conectados a Internet para hacernos la vida más fácil. La pregunta es, ¿cómo se procesa toda esa cantidad de datos? Aquí es cuando aparece la Inteligencia Artificial. La IA es la parte de las TIC dedicada al desarrollo de algoritmos que permite que una máquina tome decisiones inteligentes o, al menos, se comporte como si tuviera una inteligencia similar a la humana. El uso de la IA está presente en muchos sectores como las finanzas, la salud, el transporte o incluso la agricultura. El aprendizaje automático es una rama de la inteligencia artificial basada en la idea de que los sistemas informáticos pueden aprender por sí mismos a partir de datos. La ciencia de datos ha implementado un algoritmo de aprendizaje automático como la Red Neuronal Artificial para trabajar con estadísticas y regresión lineal para el procesamiento de datos. Una ANN es la parte de un sistema informático diseñado para simular la forma en que el cerebro humano analiza y procesa la información. Es la base de la IA y resuelve problemas que resultarían imposibles o difíciles según los estándares humanos o estadísticos. Pero, ¿Es este recurso siempre la mejor solución? Este artículo trata de una comparación entre la Red Neural Artificial Estacional con modelos clásicos como Modelo Autorregresivo Integrado de Media Móvil Estacional para el pronóstico de lluvia. El proyecto comenzó con una introducción al Aprendizaje Profundo y al Aprendizaje Automático. Posteriormente, comenzó el proceso de obtener una cantidad adecuada de datos para crear un conjunto de datos necesario. Para ello, utilizamos datos de algunos pluviómetros distribuidos en el territorio de Hauts-de-Seine del gobierno de Francia. Con datos desde el 2009 hasta 2020 de 19 sensores, el conjunto de datos se utilizó para experimentar con diferentes algoritmos y diferentes configuraciones para obtener diferentes predicciones. El rendimiento de pronóstico del modelo SARIMA y el de SANN se compararon con cuatro medidas de rendimiento: - Error de Pronóstico Medio, Error de Pronóstico Absoluto, Error Cuadrático Medio y Raíz del Error Cuadrático Medio. No solo se tendrá en cuenta la precisión del modelo, sino que también se utilizarán como punto de referencia el tiempo de ejecución y los requisitos de implementación. Finalmente, todos los modelos fueron probados en el mismo entorno de trabajo y se llegó a una conclusión gracias a los resultados obtenidos de diferentes puntos de referencia.Les tecnologies de la informació i de la comunicació han evolucionat fins al punt d'estar presents en la majoria de coses de la nostra vida quotidiana. Fins i tot l'objecte més senzill que tothom té a casa és cada vegada més intel·ligent, com ara raspalls de dents, cotxes, telèfons, etc. Tots aquests dispositius estan connectats a Internet per facilitar-nos la vida. La pregunta és: com es processa tota aquesta quantitat de dades? Aquí és quan apareix la Intel·ligència Artificial. La IA és la part de les TIC dedicada al desenvolupament d'algoritmes que permet a una màquina prendre decisions intel·ligents o, si més no, comportar-se com si tingués una intel·ligència semblant a la humana. L'ús de la IA està present en molts sectors, com el financer, la salut, el transport o fins i tot l'agricultura. L'aprenentatge automàtic és una branca de la IA basada en la idea que els sistemes informàtics poden aprendre sols a partir de dades. La ciència de les dades ha implementat un algoritme d'aprenentatge automàtic, com és la Xarxa Neuronal Artificial, per treballar amb estadístiques i regressió lineal per al processament de dades. Una ANN és la part d'un sistema informàtic dissenyat per simular la manera com el cervell humà analitza i processa la informació. És el fonament de la IA i resol problemes que resultarien impossibles o difícils per als estàndards humans o estadístics. Però, aquest recurs és sempre la millor solució? Aquest article tracta sobre una comparació entre la Xarxa Neuronal Artificial Estacionals amb models clàssics com la Model Auto Regressiu Integrat de Mitjans Mòbils Estacional per a la predicció de pluges. El projecte va començar fent una introducció a l'Aprenentatge Profund i l'Aprenentatge Automàtic. Després, es va iniciar el procés d'obtenció d'una quantitat adequada de dades per crear un conjunt de dades necessari. Per fer-ho, hem utilitzat les dades d'alguns pluviòmetres distribuïts pel territori dels Hauts-de-Seine del govern de França. Amb dades des del 2009 fins al 2020 de 19 sensors, el conjunt de dades es va utilitzar per experimentar amb diferents algoritmes i diferents configuracions per obtenir prediccions diferents. El rendiment de la predicció del model SARIMA i el de SANN es van comparar mitjançant quatre mesures de rendiment: - L'Error de Previsió Mitjà, l'Error de Previsió Absolut, l'Error Quadràtic Mig i l'Arrel de l'Error Quadràtic Mig. No només es tindrà en compte la precisió del model, sinó que també s'utilitzaran els requisits d'execució i d'implementació com a benckmark. Finalment, tots els models es van provar en el mateix entorn de treball i es va arribar a una conclusió gràcies als resultats obtinguts de diferents punts de referència

General highlight detection in sport videos

Attention is a psychological measurement of human reflection against stimulus. We propose a general framework of highlight detection by comparing attention intensity during the watching of sports videos. Three steps are involved: adaptive selection on salient features, unified attention estimation and highlight identification. Adaptive selection computes feature correlation to decide an optimal set of salient features. Unified estimation combines these features by the technique of multi-resolution autoregressive (MAR) and thus creates a temporal curve of attention intensity. We rank the intensity of attention to discriminate boundaries of highlights. Such a framework alleviates semantic uncertainty around sport highlights and leads to an efficient and effective highlight detection. The advantages are as follows: (1) the capability of using data at coarse temporal resolutions; (2) the robustness against noise caused by modality asynchronism, perception uncertainty and feature mismatch; (3) the employment of Markovian constrains on content presentation, and (4) multi-resolution estimation on attention intensity, which enables the precise allocation of event boundaries

Machine learning-based algorithms to knowledge extraction from time series data: A review

To predict the future behavior of a system, we can exploit the information collected in the past, trying to identify recurring structures in what happened to predict what could happen, if the same structures repeat themselves in the future as well. A time series represents a time sequence of numerical values observed in the past at a measurable variable. The values are sampled at equidistant time intervals, according to an appropriate granular frequency, such as the day, week, or month, and measured according to physical units of measurement. In machine learning-based algorithms, the information underlying the knowledge is extracted from the data themselves, which are explored and analyzed in search of recurring patterns or to discover hidden causal associations or relationships. The prediction model extracts knowledge through an inductive process: the input is the data and, possibly, a first example of the expected output, the machine will then learn the algorithm to follow to obtain the same result. This paper reviews the most recent work that has used machine learning-based techniques to extract knowledge from time series data