Abstract
When detecting persons from time-series camera images, shielding by obstruction (occlusion) seriously decreases the detection accuracy. For example, a person in an agricultural field represents a semi-overlapped situation by the crop and an accident with farm machinery could occur. In this paper, we propose a person detection framework to prevent accidents in an agricultural field. We use multi-camera array to acquire 3D light field of scene, and refocusing process reduces the effects of occlusion. We also use deep learning with the features of convolutional neural networks (CNNs) and classification by a support vector machine (SVM). The experimental results using datasets of a real agricultural field show the effectiveness of our approach.
