Simultaneous fusion, classification, andtraction of moving obstacles by LIDAR and camera using Bayesian algorithm

In the near future, preventing collisions with fixed or moving, alive, and inanimate obstacles will appear to be a severe challenge due to the increased use of Unmanned Ground Vehicles (UGVs). Light Detection and Ranging (LIDAR) sensors and cameras are usually used in UGV to detect obstacles. The definite tracing and classification of moving obstacles is a significant dimension in developed driver assistance systems. It is believed that the perceived model of the situation can be improved by incorporating the obstacle classification. The present study indicated a multi-hypotheses monitoring and classifying approach, which allows solving ambiguities rising with the last methods of associating and classifying targets and tracks in a highly volatile vehicular situation. This method was tested through real data from various driving scenarios and focusing on two obstacles of interest vehicle, pedestrian.In the near future, preventing collisions with fixed or moving, alive, and inanimate obstacles will appear to be a severe challenge due to the increased use of Unmanned Ground Vehicles (UGVs). Light Detection and Ranging (LIDAR) sensors and cameras are usually used in UGV to detect obstacles. The definite tracing and classification of moving obstacles is a significant dimension in developed driver assistance systems. It is believed that the perceived model of the situation can be improved by incorporating the obstacle classification. The present study indicated a multi-hypotheses monitoring and classifying approach, which allows solving ambiguities rising with the last methods of associating and classifying targets and tracks in a highly volatile vehicular situation. This method was tested through real data from various driving scenarios and focusing on two obstacles of interest vehicle, pedestrian

Human Skin Detection by Designing a Fuzzy System in Color Space

ABSTRACT one of the issues which are dealt with as an important issue in identification of a human in images is skin detection. Human skin detection with suitable speed and high accuracy is very necessary and very valuable in promoting quality of detection and can give suitable features for more correct detection. In this paper, a new fuzzy method is studied to identify human skin based on YCBCR colorful model. Skin color in YCBCR space forms a continuous set. Considering histogram of continuous set in color space, suitable membership functions are considered for fuzzy system. Based on inputs of fuzzy system, decision is made about each pixel. Tests have been performed on fei database and the obtained results show accuracy of 97% on test images