Predicting Future Location of a Moving Object

Tato práce se věnuje návrhu a implementaci aplikace pro predikci budoucí lokace pohybujícího se objektu. Popisuje metodu predikce založenou na algoritmu WhereNext. Tento algoritmus získá z databáze trajektorií objektů T-Patterny, které představují frekventované vzory pohybu objektů, a ty následně použije k predikci. Algoritmus byl implementovaný v programovacím jazyku Java a jeho funkčnost je odzkoušena na vygenerované datové sadě pohybu aut.This thesis deals with the design and the implementation of the application for predicting future location of a moving object. It describes a method for prediction based on the algorithm WhereNext. This algorithm obtains T-Patterns from a database of trajectories of objects, which represent frequent patterns of movement of objects, and those subsequently uses for prediction. The algorithm was implemented in programming language Java and its functionality was tested on a generated dataset of movement of cars.

Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking

Current multi-person localisation and tracking systems have an over reliance on the use of appearance models for target re-identification and almost no approaches employ a complete deep learning solution for both objectives. We present a novel, complete deep learning framework for multi-person localisation and tracking. In this context we first introduce a light weight sequential Generative Adversarial Network architecture for person localisation, which overcomes issues related to occlusions and noisy detections, typically found in a multi person environment. In the proposed tracking framework we build upon recent advances in pedestrian trajectory prediction approaches and propose a novel data association scheme based on predicted trajectories. This removes the need for computationally expensive person re-identification systems based on appearance features and generates human like trajectories with minimal fragmentation. The proposed method is evaluated on multiple public benchmarks including both static and dynamic cameras and is capable of generating outstanding performance, especially among other recently proposed deep neural network based approaches.Comment: To appear in IEEE Winter Conference on Applications of Computer Vision (WACV), 201

A survey on Human Mobility and its applications

Human Mobility has attracted attentions from different fields of studies such as epidemic modeling, traffic engineering, traffic prediction and urban planning. In this survey we review major characteristics of human mobility studies including from trajectory-based studies to studies using graph and network theory. In trajectory-based studies statistical measures such as jump length distribution and radius of gyration are analyzed in order to investigate how people move in their daily life, and if it is possible to model this individual movements and make prediction based on them. Using graph in mobility studies, helps to investigate the dynamic behavior of the system, such as diffusion and flow in the network and makes it easier to estimate how much one part of the network influences another by using metrics like centrality measures. We aim to study population flow in transportation networks using mobility data to derive models and patterns, and to develop new applications in predicting phenomena such as congestion. Human Mobility studies with the new generation of mobility data provided by cellular phone networks, arise new challenges such as data storing, data representation, data analysis and computation complexity. A comparative review of different data types used in current tools and applications of Human Mobility studies leads us to new approaches for dealing with mentioned challenges