An Improved Pedestrian Detection Algorithm using Integration of Resnet and Yolo V2
Geethapriya. S1, P. Kumar2
1S. Geethapriya, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, India.
2Dr. P. Kumar, Professor, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, India.
Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 2480-2485 | Volume-9 Issue-1, May 2020. | Retrieval Number: A3012059120/2020©BEIESP | DOI: 10.35940/ijrte.A3012.059120
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Pedestrian detection is one of the important tasks in object detection technology. The pedestrian detection algorithm has been used in applications like intelligent video surveillance, traffic analysis, and autonomous driving. In recent years, many pedestrian detection algorithms have been proposed but the key drawback is the accuracy and speed, which can be improved my integrating efficient algorithms. The proposed model improves the pedestrian detection algorithm by integrating two efficient algorithms together. The model is developed using the joint version of ResNet and YOLO v2, which preforms feature extraction and classification respectively. By using this model the efficiency of the system is increased by improving the accuracy rate so that it can be used with real-time applications. The model has been compared with existing models like SSD, Faster R-CNN and Mask R-CNN. Comparing with these models, the proposed model provides mAP value higher than these existing models with less loss function when tested on the INRIA dataset.
Keywords: MAP – Mean Average Precision, R-CNN – Region-based Convolutional Neural Network, ResNet – Residual Neural Network, SSD – Single Shot Detector, YOLO – You Only Look Once.
Scope of the Article: Neural Network