Publication

Multi-Object Tracking using the Particle Filter Algorithm on the Top-View Plan

Jean-Philippe Thiran
2004
Reports

Abstract

In this paper we address the problem of multi-object tracking in video sequences, with application to pedestrian tracking in a crowd. In this con- text, particle ¯lters provide a robust tracking framework under ambiguity conditions. The particle ¯lter technique is used in this work, but in order to reduce its computational complexity and increase its robustness, we propose to track the moving objects by generating hypotheses not in the image plan but on the top-view reconstruction of the scene. Compara- tive results on real video sequences show the advantage of our method for multi-object tracking.

Official source

https://infoscience.epfl.ch/entities/publication/4885119d-5269-4e7d-acd6-722983a1c02b

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Jean-Philippe Thiran
2004
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/4885119d-5269-4e7d-acd6-722983a1c02b

About this result

Related concepts (20)

Computational complexity theory

In theoretical computer science and mathematics, computational complexity theory focuses on classifying computational problems according to their resource usage, and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm. A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used.

Computational complexity

In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) and memory storage requirements. The complexity of a problem is the complexity of the best algorithms that allow solving the problem. The study of the complexity of explicitly given algorithms is called analysis of algorithms, while the study of the complexity of problems is called computational complexity theory.

Particle filter

Particle filters, or sequential Monte Carlo methods, are a set of Monte Carlo algorithms used to find approximate solutions for filtering problems for nonlinear state-space systems, such as signal processing and Bayesian statistical inference. The filtering problem consists of estimating the internal states in dynamical systems when partial observations are made and random perturbations are present in the sensors as well as in the dynamical system.

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Multi-Object Tracking using the Particle Filter Algorithm on the Top-View Plan

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