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|Title: ||Carried object detection in videos using color information|
|Authors: ||Tzanidou, Giounona|
Edirisinghe, Eran A.
|Keywords: ||Baggage detection|
Carried object detection
CIELAB color space
Viewing direction estimation
|Issue Date: ||2013|
|Publisher: ||© IEEE|
|Citation: ||TZANIDOU, G., ZAFAR, I. and EDIRISINGHE, E.A., 2013. Carried object detection in videos using color information. IEEE Transactions on Information Forensics and Security, 8 (10), pp. 1620 - 1631.|
|Abstract: ||Automatic baggage detection has become a subject
of significant practical interest in recent years. In this paper, we
propose an approach to baggage detection in CCTV video footage
that uses color information to address some of the vital shortcomings
of state-of-the-art algorithms. The proposed approach consists
of typical steps used in baggage detection, namely, the estimation
of moving direction of humans carrying baggage, construction
of human-like temporal templates, and their alignment with
the best matched view-specific exemplars. In addition, we utilize
the color information to define the region that most likely belongs
to a human torso in order to reduce the false positive detections.
A key novel contribution is the person’s viewing direction estimation
using machine learning and shoulder shape related features.
Further enhancement of baggage detection and segmentation is
achieved by exploiting the CIELAB color space properties. The
proposed system has been extensively tested for its effectiveness,
at each stage of improvement, on PETS 2006 dataset and additional
CCTVvideo footage captured to cover specific test scenarios.
The experimental results suggest that the proposed algorithm is capable
of superseding the functional performance of state-of-the-art
baggage detection algorithms.|
|Description: ||This article was published in the journal IEEE Transactions on Information Forensics and Security (© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works).|
|Version: ||Accepted for publication|
|Publisher Link: ||http://dx.doi.org/10.1109/TIFS.2013.2279797|
|Appears in Collections:||Published Articles (Computer Science)|
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