Human Detection and Tracking using HOG for Action Recognition

In today’s scenario human detection and tracking in video surveillance is important aspect because of the abnormal action detection, person identification, activity recognition etc. Detecting human beings and recognizing event in a video surveillance system plays a major role in computer vision. The...

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Bibliographic Details
Published in:Procedia computer science 2018, Vol.132, p.1317-1326
Main Authors: K, Seemanthini, S.S., Manjunath
Format: Article
Language:English
Subjects:
Action Recognition
Cluster Segmentation
Histogram of Gradient(HOG)
Object Level Segmentation
Part Level Segmentation
Support Vector Machine (SVM)
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Summary:In today’s scenario human detection and tracking in video surveillance is important aspect because of the abnormal action detection, person identification, activity recognition etc. Detecting human beings and recognizing event in a video surveillance system plays a major role in computer vision. The proposed model finds actions like walking, talking etc. This research work is mainly concentrated on detection of human object and tracking to avoid the challenges involved in difficult condition. The proposed model exhibits a new approach for the human object detection, i.e. based on Cluster segmentation approach. The considered input video will be divided into number of frames using frame generation block, followed by cluster segmentation and feature extraction. Feature extraction is done based on the Histogram of gradient. Classification will be done using Support Vector Machine algorithm; each object activity will be detected based on the result obtained by classification. The proposed model calculates accuracy of detection of each object up to 89.59%.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2018.05.048