The detection and segmentation of clouds in images taken by ground based cameras is of utmost importance for a large number of applications including all-sky imager based nowcasting systems which optimize solar power plant operation, calculation of the global irradiance, estimation of the cloud base height and support of optical satellite downlink operations. Many approaches to segment clouds in camera images are published. However, comparisons of different approaches are not frequently conducted. Here, we address this question by benchmarking six different cloud segmentation algorithms on images taken by an off-the-shelf surveillance camera. The six different algorithms include (1) a color-channel threshold-based algorithm, (2) a Clear Sky Library (CSL) based approach, (3) a region growing algorithm, (4) the Hybrid thresholding algorithm (HYTA), and a (5) novel, HYTA-based development named HYTA+. Furthermore, (6) a deep convolutional neural network (FCN) is adapted via transfer learning to this problem. The segmentation results of algorithms (1) to (5) are compared to 829 manually segmented reference images. The segmentation algorithms are benchmarked on a test dataset which is divided into 16 meteorological categories. These categories cover different Linke turbidity values, solar positions and cloud cover situations. Results show that three out of the six presented segmentation methods (CSL, HYTA+ and FCN) achieve overall accuracy values above 90%. These approaches outperform the other methods and correctly segment images with a higher consistency. Fixed threshold based methods, as the multicolor criterion, HYTA or the region growing algorithm fail under certain meteorological conditions. The FCN based segmentation (6) is tested on 160 images where it delivers the best overall pixel-by-pixel accuracy of 97.0%.