Convolutional Neural Network Based Approach to Detect Pedestrians in Real-Time videos
Sandhya N1, Anirudh Marathe2, JS Dawood Ahmed3, Aman Kumar4, Harshith R5

1Sandhya N*,Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore (Karnataka), India.
2Anirudh Marathe, Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore (Karnataka), India.
3JS Dawood Ahmed, Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore (Karnataka), India.
4Aman Kumar, Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore (Karnataka), India.
5Harshith R., Department of Computer Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore (Karnataka), India. 

Manuscript received on September 22, 2020. | Revised Manuscript received on November 03, 2020. | Manuscript published on November 10, 2021. | PP: 303-308 | Volume-10 Issue-1, November 2020 | Retrieval Number: 100.1/ijitee.A81371110120| DOI: 10.35940/ijitee.A8137.1110120
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© 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: Pedestrians in the vehicle way are in peril of being hit, along these lines making extreme damage walkers and vehicle inhabitants. Hence, constant person on foot identification was done through a set of recorded videos and the system detects the persons/pedestrians in the given input videos. In this survey, a continuous plan was proposed dependent on Aggregated Channel Features (ACF) and CPU. The proposed technique doesn’t have to resize the information picture neither the video quality. We also use SVM with HOG and SVM with HAAR to detect the pedestrians. In addition, the Convolutional Neural Networks (CNN) were trained with a set of pedestrian images datasets and later tested on some test-set of pedestrian images. The analyses demonstrated that the proposed technique could be utilized to distinguish people on foot in the video with satisfactory mistake rates and high prediction accuracy. In this manner, it tends to be applied progressively for any real-time streaming of videos and also for prediction of pedestrians in pre-recorded videos. 
Keywords: Aggregated Channel Features (ACF), Convolutional Neural Networks (CNN), HAAR, Pedestrians, SVM(Support Vector Machine).