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Integrating Millimeter Wave Radar with a Monocular Vision Sensor for On-Road Obstacle Detection Applications
Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, China
* Author to whom correspondence should be addressed.
Received: 19 August 2011; in revised form: 15 September 2011 / Accepted: 15 September 2011 / Published: 21 September 2011
Abstract: This paper presents a systematic scheme for fusing millimeter wave (MMW) radar and a monocular vision sensor for on-road obstacle detection. As a whole, a three-level fusion strategy based on visual attention mechanism and driver’s visual consciousness is provided for MMW radar and monocular vision fusion so as to obtain better comprehensive performance. Then an experimental method for radar-vision point alignment for easy operation with no reflection intensity of radar and special tool requirements is put forward. Furthermore, a region searching approach for potential target detection is derived in order to decrease the image processing time. An adaptive thresholding algorithm based on a new understanding of shadows in the image is adopted for obstacle detection, and edge detection is used to assist in determining the boundary of obstacles. The proposed fusion approach is verified through real experimental examples of on-road vehicle/pedestrian detection. In the end, the experimental results show that the proposed method is simple and feasible.
Keywords: visual attention; monocular vision; millimeter wave radar; autonomous ground vehicle; obstacle detection
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MDPI and ACS Style
Wang, T.; Zheng, N.; Xin, J.; Ma, Z. Integrating Millimeter Wave Radar with a Monocular Vision Sensor for On-Road Obstacle Detection Applications. Sensors 2011, 11, 8992-9008.
Wang T, Zheng N, Xin J, Ma Z. Integrating Millimeter Wave Radar with a Monocular Vision Sensor for On-Road Obstacle Detection Applications. Sensors. 2011; 11(9):8992-9008.
Wang, Tao; Zheng, Nanning; Xin, Jingmin; Ma, Zheng. 2011. "Integrating Millimeter Wave Radar with a Monocular Vision Sensor for On-Road Obstacle Detection Applications." Sensors 11, no. 9: 8992-9008.