Simultaneous Classification and Traction of Moving Obstacles by LIDAR And Camera Using Bayesian Algorithm
محورهای موضوعی : A.2. Control Structures and Microprogramming
Masrour Dowlatabadi
1
(Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Ahmad Afshar
2
(Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran)
Ali Moarefianpour
3
(Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran)
کلید واژه: Bayesian Algorithm, Simultaneous classification and traction, LIDAR sensor and camera,
چکیده مقاله :
Shortly, 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 tracing and classification of moving obstacles is a significant dimension in developed driver assistance systems. 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. We proposed a recursive method based on Bayesian Algorithm for using classification information of obstacles in the tracking information of them and vice versa. The results are shown that the proposed method can improve classifying and tracking together.This method was tested through real data from various driving scenarios and focusing on two obstacles of interest vehicle and pedestrian.
