Effective Deep Learning Based Architecture for Pedestrian Detection from Digital Images
Khushaboo Gill1, Veenu Mangat2

1*Ms. Khushaboo Gill, M.E. (I.T.), U.I.E.T., Panjab University, Chandigarh, India.
2Dr. Veenu Mangat, Associate Professor (I.T.), U.I.E.T., Panjab University, Chandigarh, India.
Manuscript received on February 06, 2020. | Revised Manuscript received on February 10, 2020. | Manuscript published on February 30, 2020. | PP: 1498-1508 | Volume-9 Issue-3, February, 2020. | Retrieval Number: B4225129219/2020©BEIESP | DOI: 10.35940/ijeat.B4225.029320
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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: This paper is to present an efficient and fast deep learning algorithm based on neural networks for object detection and pedestrian detection. The technique, called MobileNet Single Shot Detector, is an extension to Convolution Neural Networks. This technique is based on depth-wise distinguishable convolutions in order to build a lightweighted deep convolution network. A single filter is applied to each input and outputs are combined by using pointwise convolution. Single Shot Multibox Detector is a feed forward convolution network that is combined with MobileNets to give efficient and accurate results. MobileNets combined with SSD and Multibox Technique makes it much faster than SSD alone can work. The accuracy for this technique is calculated over colored (RGB images) and also on infrared images and its results are compared with the results of shallow machine learning based feature extraction plus classification technique viz. HOG plus SVM technique. The comparison of performance between proposed deep learning and shallow learning techniques has been conducted over benchmark dataset and validation testing over own dataset in order measure efficiency of both algorithms and find an effective algorithm that can work with speed and accurately to be applied for object detection in real world pedestrian detection application.
Keywords: Convolution network, Deep Learning, Histogram of Oriented Gradients, Object Detection, Pedestrian Detection, Multibox Detector.