Version 1
: Received: 24 May 2019 / Approved: 29 May 2019 / Online: 29 May 2019 (11:19:19 CEST)
How to cite:
Ali, T.; Alreshidi, E. Identifying Human Behavious Using Deep Trajectory Descriptors. Preprints2019, 2019050350. https://doi.org/10.20944/preprints201905.0350.v1
Ali, T.; Alreshidi, E. Identifying Human Behavious Using Deep Trajectory Descriptors. Preprints 2019, 2019050350. https://doi.org/10.20944/preprints201905.0350.v1
Ali, T.; Alreshidi, E. Identifying Human Behavious Using Deep Trajectory Descriptors. Preprints2019, 2019050350. https://doi.org/10.20944/preprints201905.0350.v1
APA Style
Ali, T., & Alreshidi, E. (2019). Identifying Human Behavious Using Deep Trajectory Descriptors. Preprints. https://doi.org/10.20944/preprints201905.0350.v1
Chicago/Turabian Style
Ali, T. and Eissa Alreshidi. 2019 "Identifying Human Behavious Using Deep Trajectory Descriptors" Preprints. https://doi.org/10.20944/preprints201905.0350.v1
Abstract
Identifying human actions in complex scenes is widely considered as a challenging research problem due to the unpredictable behaviors and variation of appearances and postures. For extracting variations in motion and postures, trajectories provide meaningful way. However, simple trajectories are normally represented by vector of spatial coordinates. In order to identify human actions, we must exploit structural relationship between different trajectories. In this paper, we propose a method that divides the video into N number of segments and then for each segment we extract trajectories. We then compute trajectory descriptor for each segment which capture the structural relationship among different trajectories in the video segment. For trajectory descriptor, we project all extracted trajectories on the canvas. This will result in texture image which can store the relative motion and structural relationship among the trajectories. We then train Convolution Neural Network (CNN) to capture and learn the representation from dense trajectories. . Experimental results shows that our proposed method out performs state of the art methods by 90.01% on benchmark data set.
Keywords
Support vector machine, motion descriptor, features, human behaviors
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.