A Comparative Evaluation of Three Skin Color Detection Approaches

Skin segmentation is a challenging task due to several influences such as unknown lighting conditions, skin colored background, and camera limitations. A lot of skin segmentation approaches were proposed in the past including adaptive (in the sense of updating the skin color online) and non-adaptive approaches. In this paper, we compare three skin segmentation approaches that are promising to work well for hand tracking, which is our main motivation for this work. Hand tracking can widely be used in VR/AR e.g. navigation and object manipulation. The first skin segmentation approach is a well-known non-adaptive approach. It is based on a simple, pre-computed skin color distribution. Methods two and three adaptively estimate the skin color in each frame utilizing clustering algorithms. The second approach uses a hierarchical clustering for a simultaneous image and color space segmentation, while the third approach is a pure color space clustering, but with a more sophisticated clustering approach. For evaluation, we compared the segmentation results of the approaches against a ground truth dataset. To obtain the ground truth dataset, we labeled about 500 images captured under various conditions.

Comparing sensor-based and camera-based approaches to recognizing the occupancy status of the load handling device of forklift trucks

Real-time data of key performance enablers in logistics warehouses are of growing importance as they permit decision-makers to instantaneously react to alerts, deviations and damages. Several technologies appear as adequate data sources to collect the information required in order to achieve the goal. In the present re-search paper, the load status of the fork of a forklift is to be recognized with the help of a sensor-based and a camera-based solution approach. The comparison of initial experimentation results yields a statement about which direction to pursue for promising further research.