Open AccessConstruction of LWCNN Framework and its Application to Pedestrian Detection with Segmentation Process
Author(s) -
R. Kanthavel
Publication year - 2021
Publication title -
journal of innovative image processing
Language(s) - English
Resource type - Journals
ISSN - 2582-4252
DOI - 10.36548/jiip.2021.3.008
Subject(s) - computer science , support vector machine , segmentation , artificial intelligence , classifier (uml) , pedestrian detection , pedestrian , pattern recognition (psychology) , object detection , computer vision , data mining , machine learning , engineering , transport engineering
To solve the challenges in traffic object identification, fuzzification, and simplification in a real traffic environment, it is highly required to develop an automatic detection and classification technique for roads, automobiles, and pedestrians with multiple traffic objects inside the same framework. The proposed method has been evaluated on a database with complicated poses, motions, backgrounds, and lighting conditions for an urban scenario where pedestrians are not obstructed. The suggested CNN classifier has an FPR of less than that of the SVM classifier. Confirming the significance of automatically optimized features, the SVM classifier's accuracy is equal to that of the CNN. The proposed framework is integrated with the additional adaptive segmentation method to identify pedestrians more precisely than the conventional techniques. Additionally, the proposed lightweight feature mapping leads to faster calculation times and it has also been verified and tabulated in the results and discussion section.