Martin Israel

Parameter-free clustering: Application to fawns detection

Many fawns and other wild animals are killed by mowing machines every year. To prevent them from being killed or injured, a sensor system is being developed to detect the fawns hidden in meadows under mowing. Beside a microwave radar system, two cameras (thermal infrared and RGB) take a picture at the mowers current location. This contribution focuses on the compression-based algorithm that will be adopted to detect the locations containing a fawn hiding in the grass: such approach, being parameter-free, allows performing a fully unsupervised clustering by exploiting the intrinsic properties of data compression to estimate the amount of shared information between two images.

Detecting nests of lapwing birds with the aid of a small unmanned aerial vehicle with thermal camera

Due to increasingly dense agricultural land use, many ground breeding birds are threatened with extinction or near threatened. We present a new method, to detect lapwing and other ground breeding bird nests. A small UAV with a thermal camera is suitable to detect a lapwing nest from a flight altitude of 40 m. With the aid of a special thermal camera image preprocessing algorithm the contrast could be enhanced by 10 to 50% which improves the detectability of nests. We successfully demonstrated, that we detected 93% of the known nests. This paper describes and analyzes the necessary conditions for optimal lapwing nest detection with a UAV borne thermal camera.