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2017 | OriginalPaper | Chapter

Unsupervised Feature Descriptors Based Facial Tracking over Distributed Geospatial Subspaces

Authors : Shubham Dokania, Ayush Chopra, Feroz Ahmad, S. Indu, Santanu Chaudhury

Published in: Pattern Recognition and Machine Intelligence

Publisher: Springer International Publishing

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Abstract

Object Tracking has primarily been characterized as the study of object motion trajectory over constraint subspaces under attempts to mimic human efficiency. However, the trend of monotonically increasing applicability and integrated relevance over distributed commercial frontiers necessitates that scalability be addressed. The present work proposes a system for fast large scale facial tracking over distributed systems beyond individual human capabilities leveraging the computational prowess of large scale processing engines such as Apache Spark. The system is pivoted on an interval based approach for receiving the input feed streams, which is followed by a deep encoder-decoder network for generation of robust environment invariant feature encoding. The system performance is analyzed while functionally varying various pipeline components, to highlight the robustness of the vector representations and near real-time processing performance.

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previous chapter Abnormal Crowd Behavior Detection Based on Combined Approach of Energy Model and Threshold
next chapter Face Detection Based on Frequency Domain Features
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Metadata
Title
Unsupervised Feature Descriptors Based Facial Tracking over Distributed Geospatial Subspaces
Authors
Shubham Dokania
Ayush Chopra
Feroz Ahmad
S. Indu
Santanu Chaudhury
Copyright Year
2017
Book
Pattern Recognition and Machine Intelligence

Print ISBN: 978-3-319-69899-1

Electronic ISBN: 978-3-319-69900-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-69900-4_25

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