Feature compression: a framework for multi-view multi-person tracking in visual sensor networks

Coşar, Serhan and Çetin, Müjdat (2014) Feature compression: a framework for multi-view multi-person tracking in visual sensor networks. Journal of Visual Communication and Image Representation, 25 (5). pp. 864-873. ISSN 1047-3203 (Print) 1095-9076 (Online)

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Visual sensor networks (VSNs) consist of image sensors, embedded processors and wireless transceivers which are powered by batteries. Since the energy and bandwidth resources are limited, setting up a tracking system in VSNs is a challenging problem. In this paper, we present a framework for human tracking in VSNs. The traditional approach of sending compressed images to a central node has certain disadvantages such as decreasing the performance of further processing (i.e., tracking) because of low quality images. Instead, we propose a feature compression-based decentralized tracking framework that is better matched with the further inference goal of tracking. In our method, each camera performs feature extraction and obtains likelihood functions. By transforming to an appropriate domain and taking only the significant coefficients, these likelihood functions are compressed and this new representation is sent to the fusion node. As a result, this allows us to reduce the communication in the network without significantly affecting the tracking performance. An appropriate domain is selected by performing a comparison between well-known transforms. We have applied our method for indoor people tracking and demonstrated the superiority of our system over the traditional approach and a decentralized approach that uses Kalman filter.
Item Type: Article
Uncontrolled Keywords: Visual sensor networks; Camera networks; Human tracking; Decentralized tracking; Communication constraints; Feature compression; Compressing likelihood functions; Bandwidth-efficient tracking
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
Depositing User: Müjdat Çetin
Date Deposited: 22 Dec 2014 10:42
Last Modified: 26 Apr 2022 09:18
URI: https://research.sabanciuniv.edu/id/eprint/25619

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