Novel Approach for Person Detection Based on Image Segmentation Neural Network

Abstract

With the rise of the modern possibilities in computer science and device engineering, as well as with growing population in big cities among the world, a lot of new approaches for person detection have become a very interesting topic. In this paper, two different approaches for person detection are tested and compared. As the first and standard approach, the YOLO architectures, which are very effective for image classification, are adapted to the detection problem. The second and novel approach is based on the encoder-decoder scheme causing the image segmentations, in combination with the locator. The locator part is supposed to find local maxima in segmented image and should return the specific coordinates representing the head centers in the original image. Results clearly report this approach with U-Net used as encoder-decoder scheme with the locator based on local peaks as the more accurately performing detection technique, in comparison to YOLO architectures.