Abstract
In this work, an algorithm that detects pedestrians in still images using different classifiers is presented. HOG, which is frequently used in pedestrian detection, and support vector machine (SVM), K nearest neighbors (KNN) and AdaBoost algorithms were used as descriptors. It is decided whether the image is pedestrian by looking at the result of three different classifiers. In order to demonstrate the effectiveness of the method, the system is trained using the INRIA data set and tested by using Penn Fudan Pedestrian Dataset which is different dataset. Experimental results show that the proposed method detects higher accuracy than pedestrian detection using a single classifier.
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.
