Algorithms for Multiple Object Tracking

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Introduction

The aim of this thesis is to define objective methods for measuring the performance of multiple object tracking algorithms in real datasets.

An important implementation of the theories proposed in the literature will be the extension of the methodology to multi-camera systems.

Planned Activities

The initial part of this work will consist of a research in literature on the state of the art for multiple object tracking.

The second part of this thesis project will be dedicated to the study of the existing metrics for the evaluation and comparison of these algorithms in terms of accuracy and calculation times.

The final aim will be the definition of a workflow for the testing of these new algorithms and the evaluation of them on similar real datasets.

It is required that the candidate has significant mathematical foundations, analytical skills and excellent programming skills, especially in Python.

The candidate will be supported by highly qualified personnel and when necessary will be able to access the company computing resources (IBM PowerAI equipped with NVIDIA P100 and V100 cards).

Who we’re looking for

Students that are about to get their master degree in: Computer Science, Mathematics

Skills: Python, C/C++, math, preferred knowledge of  TensorFlow / PyTorch / BigDL

Duration of this Projects: 6-8 months

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