Abstract
In order to solve the problems of traffic object detection, fuzzification, and simplification in real traffic environment, an automatic detection and classification algorithm for roads, vehicles, and pedestrians with multiple traffic objects under the same framework is proposed. We construct the final V view through a considerate U-V view method, which determines the location of the horizon and the initial contour of the road. Road detection results are obtained through error label reclassification, omitting point reassignment, and so an. We propose a peripheral envelope algorithm to determine sources of vehicles and pedestrians on the road. The initial segmentation results are determined by the regional growth of the source point through the minimum neighbor similarity algorithm. Vehicle detection results on the road are confirmed by combining disparity and color energy minimum algorithms with the object window aspect ratio threshold method. A method of multifeature fusion is presented to obtain the pedestrian target area, and the pedestrian detection results on the road are accurately segmented by combining the disparity neighbor similarity and the minimum energy algorithm. The algorithm is tested in three datasets of Enpeda, KITTI, and Daimler; then, the corresponding results prove the efficiency and accuracy of the proposed approach. Meanwhile, the real-time analysis of the algorithm is performed, and the average time efficiency is 13 pfs, which can realize the real-time performance of the detection process.
Highlights
With the rapid development of driverless and assisted driving technologies, autonomous vehicles should have safety functions such as obstacle collision warning, road departure warning, and speed maintenance [1]; they can analyze and understand the environment around them and discriminate roads, cars, pedestrians, buildings, and so on in a traffic scene [2]
Rough detection of the road surface is achieved through inverse transformation to obtain the initial contour of the road, as shown in Figure 5(c), but there are still spot spots that are misclassified or missing. erefore, we propose to reclassify road spots that may be misjudged or missed based on the initial contour of the road by reclassifying the wrong class and reassigning missing points to obtain road detection results. e specific method is as follows: Complexity
In the KITTI and Daimler datasets, the detection results of our method are significantly better than the NT-RD method
Summary
With the rapid development of driverless and assisted driving technologies, autonomous vehicles should have safety functions such as obstacle collision warning, road departure warning, and speed maintenance [1]; they can analyze and understand the environment around them and discriminate roads, cars, pedestrians, buildings, and so on in a traffic scene [2]. Stereo vision-based road detection can obtain accurate road contour estimates and provide clear path driving information, which has been studied for many years. Zhang et al [20] proposed a Dijkstra road model based on vanishing point constraints to implement stereo vision road detection. (i) For the complex problem of traffic multiobject classification detection in traffic environment perception, we propose to realize the automatic detection and classification of roads, vehicles, and pedestrians under a common framework, which avoids the detection of a single traffic object or the detection of general obstacles and improves the pertinence of detection objects. (ii) A new disparity image correction method is proposed to provide conditions for accurate classification and detection of subsequent traffic objects such as roads, vehicles, and pedestrians. Given the value Δ ul − ur, we can obtain disparity: b
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
