Abstract
Background Thresholding is a method used to improvise the training of a model with multiple datasets. The primary objective of this paper is to test the scalability of the Background Thresholding approach incorporated with a proposed object detection model for multiple datasets. The proposed research work has taken the Bosch traffic light dataset for traffic light detection, Tsinghua-Tencent 100K Dataset for Traffic sign detection and Udacity annotated driving dataset for car detection. Different algorithms and deep learning frameworks are experimented to provide a detailed analysis as to which model performs the best for the particular task at hand. The proposed research work is concluded with the Faster-RCNN (Regional-CNN) algorithm, which is coupled with Inception V2 to remain as the best for our approach.
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.
