Session 2F: Pedestrian Detection using Convolutional Neural Networks
Session Number
Session 2F: 2nd Presentation
Advisor(s)
Dr. Miles Wernic, Illinois Institute of Technology
Location
Room A113
Start Date
26-4-2018 10:35 AM
End Date
26-4-2018 11:20 AM
Abstract
The purpose of this investigation was to devise an efficient and accurate algorithm that is capable of detecting pedestrians’ positions within frames of live footage. After exploring various models for pedestrian detection and weighing their advantages, we settled on using a convolutional neural network (CNN) as the basis for our algorithm. Using pre-recorded footage from the Chicago Police Department as well as the California Institute of Technology’s pedestrian image dataset, we are currently training a CNN to recognize pedestrians within 640 by 480 pixel still images. Currently, the model can predict (with decent time efficiency on a Nvidia GeForce 940M processor) whether a pedestrian is present within the image, but its accuracy rates are concerning due to a lack of pedestrian data from the CPD and the awkward viewing angle of the Caltech footage. We are in the process of transitioning our model to its final stage, which will involve a regional proposal network in conjunction with the convolutional neural network. After training this final model with ample and more suitable data, our final results will be presented at IMSAloquium.
Session 2F: Pedestrian Detection using Convolutional Neural Networks
Room A113
The purpose of this investigation was to devise an efficient and accurate algorithm that is capable of detecting pedestrians’ positions within frames of live footage. After exploring various models for pedestrian detection and weighing their advantages, we settled on using a convolutional neural network (CNN) as the basis for our algorithm. Using pre-recorded footage from the Chicago Police Department as well as the California Institute of Technology’s pedestrian image dataset, we are currently training a CNN to recognize pedestrians within 640 by 480 pixel still images. Currently, the model can predict (with decent time efficiency on a Nvidia GeForce 940M processor) whether a pedestrian is present within the image, but its accuracy rates are concerning due to a lack of pedestrian data from the CPD and the awkward viewing angle of the Caltech footage. We are in the process of transitioning our model to its final stage, which will involve a regional proposal network in conjunction with the convolutional neural network. After training this final model with ample and more suitable data, our final results will be presented at IMSAloquium.