An Efficient Multiple Object Detection and Tracking Framework for Automatic Counting and Video Surveillance Applications

Blanco Adán, Carlos Roberto del and Jaureguizar Núñez, Fernando and García Santos, Narciso (2012). An Efficient Multiple Object Detection and Tracking Framework for Automatic Counting and Video Surveillance Applications. "IEEE Transactions on Consumer Electronics", v. 58 (n. 3); pp. 857-862. ISSN 0098-3063. https://doi.org/10.1109/TCE.2012.6311328.

Description

Title: An Efficient Multiple Object Detection and Tracking Framework for Automatic Counting and Video Surveillance Applications
Author/s:
  • Blanco Adán, Carlos Roberto del
  • Jaureguizar Núñez, Fernando
  • García Santos, Narciso
Item Type: Article
Título de Revista/Publicación: IEEE Transactions on Consumer Electronics
Date: 25 September 2012
ISSN: 0098-3063
Volume: 58
Subjects:
Freetext Keywords: Moving object detection, multiple object tracking, object counting, video surveillance applications, particle filtering, IP cameras, real-time applications.
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition

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Abstract

Automatic visual object counting and video surveillance have important applications for home and business environments, such as security and management of access points. However, in order to obtain a satisfactory performance these technologies need professional and expensive hardware, complex installations and setups, and the supervision of qualified workers. In this paper, an efficient visual detection and tracking framework is proposed for the tasks of object counting and surveillance, which meets the requirements of the consumer electronics: off-the-shelf equipment, easy installation and configuration, and unsupervised working conditions. This is accomplished by a novel Bayesian tracking model that can manage multimodal distributions without explicitly computing the association between tracked objects and detections. In addition, it is robust to erroneous, distorted and missing detections. The proposed algorithm is compared with a recent work, also focused on consumer electronics, proving its superior performance.

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