Sánchez Almodóvar, Nuria and Cuerdo, Alejandro and Sastre García, Daniel and Alfonso Kurano, Jorge and Menendez Garcia, Jose Manuel
Multisensor data fusion for accurate modelling of mobile objects.
In: "Symposium on Emerged/Emerging "Disruptive" Technologies", 09/05/2014 - 10/05/2014, Madrid, Spain. pp. 1-9.
In the last decade, multi-sensor data fusion has become a broadly demanded discipline to achieve advanced solutions that can be applied in many real world situations, either civil or military. In Defence,accurate detection of all target objects is fundamental to maintaining situational awareness, to locating threats in the battlefield and to identifying and protecting strategically own forces. Civil applications, such as traffic monitoring, have similar requirements in terms of object detection and reliable identification of incidents in order to ensure safety of road users. Thanks to the appropriate data fusion technique, we can give these systems the power to exploit automatically all relevant information from
multiple sources to face for instance mission needs or assess daily supervision operations. This paper
focuses on its application to active vehicle monitoring in a particular area of high density traffic, and how
it is redirecting the research activities being carried out in the computer vision, signal processing and
machine learning fields for improving the effectiveness of detection and tracking in ground surveillance
scenarios in general. Specifically, our system proposes fusion of data at a feature level which is extracted
from a video camera and a laser scanner. In addition, a stochastic-based tracking which introduces some
particle filters into the model to deal with uncertainty due to occlusions and improve the previous detection output is presented in this paper. It has been shown that this computer vision tracker contributes to detect objects even under poor visual information. Finally, in the same way that humans are able to
analyze both temporal and spatial relations among items in the scene to associate them a meaning, once
the targets objects have been correctly detected and tracked, it is desired that machines can provide a
trustworthy description of what is happening in the scene under surveillance. Accomplishing so ambitious
task requires a machine learning-based hierarchic architecture able to extract and analyse behaviours at
different abstraction levels. A real experimental testbed has been implemented for the evaluation of the
proposed modular system. Such scenario is a closed circuit where real traffic situations can be simulated.
First results have shown the strength of the proposed system.