Deep learning for people detection on beach images

Convolutional architectures have in recent years become state-of-the-art for several object detection tasks. However, these detectors have not yet been evaluated for detection and monitoring of beach areas. As some of these areas need to be continually monitored for dangerous situations, such as shark attacks, an automated system would be an effective risk control measure. The most significant and specific challenges for this problem are variable scene illumination, partial occlusion and distant camera position. In this work we present a study on three recent convolutional architectures for the task of people detection in beach scenarios. Our dataset is composed of images taken in the Boa Viagem beach, in Brazil, and is used to evaluate Faster R-CNN, R-FCN and SSD in terms of quality and speed of detection. The detectors are pretrained on a dataset containing 91 classes of objects, including people with different levels of scale and occlusion. The results suggest that the Faster R-CNN meta-architecture with the Resnet 101 feature extractor generates significantly better detections in terms of F-measure, while performing at 5.6 fps on a GTX 1080 Ti GPU.