Comparative Analysis of Hand-Coded and Automated Feature Based Algorithms for Human Detection in Embedded Application

Human detection in the video frame is a difficult task and time consuming process. Inherent characteristics like posture, mien, physique and alien features like radiance, blockage, cluster, litter are prominent factors effecting the task. The main aim of this paper is to implement, compute and compa...

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Bibliographic Details
Published in2018 3rd International Conference for Convergence in Technology (I2CT) pp. 1 - 6
Main Authors Agarwal, Naman, Suthar, Sanjay, Jain, Harshita, Godara, Pratiti, Vaidya, Bhaumik
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2018
Subjects
Classification algorithms
Deep Learning
Detectors
Feature extraction
Haar Cascade
Histogram of Oriented Gradient
Histograms
Machine Learning
Microsoft Windows
Object detection
Single Shot Multibox Detector
Support Vector Machine
Support vector machines
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DOI10.1109/I2CT.2018.8529577

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Summary:Human detection in the video frame is a difficult task and time consuming process. Inherent characteristics like posture, mien, physique and alien features like radiance, blockage, cluster, litter are prominent factors effecting the task. The main aim of this paper is to implement, compute and compare different algorithms in unfavorable conditions and conclude the output parameters of the algorithm on real time videos. In this paper, three feature based algorithms Single Shot Multibox Detector (SSD), Haar Cascade and Histogram of Oriented Gradient (HOG) are computed. The comparison of this algorithms in real life scenarios and on different platforms are being computed to find out which algorithm is suitable for an embedded application.
DOI:10.1109/I2CT.2018.8529577