Soft biometrics in low resolution and low quality CCTV videos

Soft biometrics are biometric traits that do not offer exact human identification, however, they can provide adequate information to narrow-down the search space and give valuable insights for the subject in question. In this work, we examine the issues that emerge by analysing CCTV videos for soft...

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Bibliographic Details
Published in:7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016) p. 24
Main Authors: Semertzidis, T, Axenopoulos, A, Karadimos, P, Daras, P
Format: Conference Proceeding
Language:English
Published: Stevenage, UK IET 2016
The Institution of Engineering & Technology
Subjects:
Annotations
Biometrics
Closed circuit television
Computer vision and image processing techniques
Datasets
Image and video coding
Image recognition
Image segmentation
Security management
Video
Video signal processing
segmentation mask
low resolution-low quality CCTV video
video cameras
video coding
biometrics (access control)
closed circuit television
quality encoding
surveillance image colour variation
image segmentation
surveillance image light variation
soft biometric extraction
image colour analysis
Exemplar concept
video surveillance
image resolution
ISBN:1785614002, 9781785614002
Online Access:Get full text
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Summary:Soft biometrics are biometric traits that do not offer exact human identification, however, they can provide adequate information to narrow-down the search space and give valuable insights for the subject in question. In this work, we examine the issues that emerge by analysing CCTV videos for soft biometrics and propose a methodology for extracting soft biometrics from low-quality and low-resolution video footage taken from real, street CCTV cameras. The proposed approach is based on the concept of Exemplars, that is, to find matches of the examined subject over a labelled dataset, which is able to encode the quality, colour and light variations of the surveillance images. Experiments have been conducted in a new challenging dataset that we introduce in this paper. It has been created using real CCTV footage, enhanced with a wide range of annotations from multiple people, and a manually created segmentation mask for each detection/person. This dataset is made available to scientific community for comparison and improvement of their methodologies in real-world scenarios.
Bibliography:ObjectType-Article-1
ObjectType-Feature-2
SourceType-Conference Papers & Proceedings-1
content type line 22
ISBN:1785614002
9781785614002
DOI:10.1049/ic.2016.0092