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

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.