Structural similarity-based object tracking in multimodality surveillance videos |
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Authors: | Artur Łoza Lyudmila Mihaylova David Bull Nishan Canagarajah |
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Affiliation: | (1) Department of Electrical and Electronic Engineering, University of Bristol, Bristol, BS8 1UB, UK;(2) Department of Communication Systems, Lancaster University, Lancaster, LA1 4WA, UK |
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Abstract: | This paper addresses the problem of object tracking in video sequences for surveillance applications by using a recently proposed
structural similarity-based image distance measure. Multimodality surveillance videos pose specific challenges to tracking
algorithms, due to, for example, low or variable light conditions and the presence of spurious or camouflaged objects. These
factors often cause undesired luminance and contrast variations in videos produced by infrared sensors (due to varying thermal
conditions) and visible sensors (e.g., the object entering shadowy areas). Commonly used colour and edge histogram-based trackers
often fail in such conditions. In contrast, the structural similarity measure reflects the distance between two video frames
by jointly comparing their luminance, contrast and spatial characteristics and is sensitive to relative rather than absolute
changes in the video frame. In this work, we show that the performance of a particle filter tracker is improved significantly
when the structural similarity-based distance is applied instead of the conventional Bhattacharyya histogram-based distance.
Extensive evaluation of the proposed algorithm is presented together with comparisons with colour, edge and mean-shift trackers
using real-world surveillance video sequences from multimodal (infrared and visible) cameras. |
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Keywords: | Structural similarity measure Object tracking Video sequences Particle filtering Colour and edge cues Multimodal data |
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