A great development of technologies for the detection of buried objects took place in the last years. Applications in archeology, finding of pipe lines and others were important, but most attention was paid in humanitarian detection of land mines and unexploded ordnances. Among these technologies, thermography is one of the most useful techniques and has been applied concurrent with other ones (Ground Penetrating Radar, Electromagnetic Induction, etc.) We have made several experiments to obtain thermographic images of buried objects in the middle and far infrared, in laboratory and in field, and in different types of terrain: naked ground, ground covered with grass and sand. We employed, as warming methods, natural sun radiation and blowing of warm air or halogen lamps. We have used metallic and dielectric objects of different sizes and shapes so as to recognize them by their characteristics. The acquired images were improved using noise reduction and image enhancement techniques.
In this work we present the thermographic images obtained. All measurements were made at short distance, less than 100 cm, as the objective of our work is to develop a thermographic imaging system for the detection of buried objects to be installed in an autonomous ground robot.
Exploiting a new distributed cooperative processing scheme where multiple processors cooperate in finding a global minimum, we have developed a new efficient maximum likelihood (ML) based calculation method for multitarget motion analysis under a fixed networked multisensor environment. The Track estimation of targets from sensor is a crucial issue in active dynamic scene understanding. Multitarget motion analysis, where there are multiple moving targets and multiple fixed sensors which only measure bearings of the targets, is to associate targets and sensor data, and estimate target tracks based on that association. This is NP-hard problem to obtain the optimal solution, as the method easily gets trapped in one of local optima. We applied the decentralized cooperative search technique to this problem, and proved our method effective. The method uses more than one processor, each of which has its own partial search space, searching multiple possibilities in parallel. This paper shows the current status of our research, and presents two prototypes of cooperative multi-agent systems for extended multi-target motion analysis.