Human Detection from Aerial Imagery for Automatic Counting of Shellfish Gatherers

Automatic human identification from aerial image time series or video sequences is a challenging issue. We propose here a complete processing chain that operates in the context of recreational shellfish gatherers counting in a coastal environment (the Gulf of Morbihan, South Brittany, France). It starts from a series of aerial photographs and builds a mosaic in order to prevent multiple occurrences of the same objects on the overlapping parts of aerial images. To do so, several stitching techniques are reviewed and discussed in the context of large aerial scenes. Then people detection is addressed through a sliding window analysis combining the HOG descriptor and a supervised classifier. Several classification methods are compared, including SVM, Random Forests, and AdaBoost. Experimental results show the interest of the proposed approach, and provides directions for future research.