Ms. Cigdem Beyan Profile

Cigdem Beyan

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SPIE Journal Paper | 1 July 2011

Fusion of thermal- and visible-band video for abandoned object detection

Cigdem Beyan, Ahmet Yigit, Alptekin Temizel

JEI, Vol. 20, Issue 03, 033001, (July 2011) https://doi.org/10.1117/12.10.1117/1.3602204

KEYWORDS: Video, Cameras, Image fusion, Thermography, Reflectivity, Video surveillance, Binary data, Environmental sensing, Detection and tracking algorithms, Surveillance systems

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Timely detection of packages that are left unattended in public spaces is a security concern, and rapid detection is important for prevention of potential threats. Because constant surveillance of such places is challenging and labor intensive, automated abandoned-object-detection systems aiding operators have started to be widely used. In many studies, stationary objects, such as people sitting on a bench, are also detected as suspicious objects due to abandoned items being defined as items newly added to the scene and remained stationary for a predefined time. Therefore, any stationary object results in an alarm causing a high number of false alarms. These false alarms could be prevented by classifying suspicious items as living and nonliving objects. In this study, a system for abandoned object detection that aids operators surveilling indoor environments such as airports, railway or metro stations, is proposed. By analysis of information from a thermal- and visible-band camera, people and the objects left behind can be detected and discriminated as living and nonliving, reducing the false-alarm rate. Experiments demonstrate that using data obtained from a thermal camera in addition to a visible-band camera also increases the true detection rate of abandoned objects.

Proceedings Article | 26 May 2011 Paper

Mean-shift tracking for surveillance applications using thermal and visible band data fusion

Cigdem Beyan, Alptekin Temizel

Proceedings Volume 8020, 802010 (2011) https://doi.org/10.1117/12.882838

KEYWORDS: Thermography, Automatic tracking, Detection and tracking algorithms, Cameras, Image segmentation, Video, Video surveillance, Binary data, Image fusion, Error analysis

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Separate tracking of objects such as people and the luggages they carry is important for video surveillance applications as it would allow making higher level inferences and timely detection of potential threats. However, this is a challenging problem and in the literature, people and objects they carry are tracked as a single object. In this study, we propose using thermal imagery in addition to the visible band imagery for tracking in indoor applications (such as airports, metro or railway stations). We use adaptive background modeling in association with mean-shift tracking for fully automatic tracking. Trackers are refreshed using the background model to handle occlusion and split and to detect newly emerging objects as well as objects that leave the scene. Visible and thermal domain tracking information are fused to allow tracking of people and the objects they carry separately using their heat signatures. By using the trajectories of these objects, interactions between them could be deduced and potential threats such as abandoning of an object by a person could be detected in real-time. Better tracking performance is also achieved compared to using a single modality as thermal reflection and halo effect which adversely affect tracking are eliminated by the complementing visible band data. The proposed method has been tested on videos containing various scenarios. The experimental results show that the presented method is effective for separate tracking of objects such as people and their belongings and for detecting the interactions in the presence of occlusions.

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