Structural similarity-based object tracking in multimodality surveillance videos

Authors:
Artur Łoza;Lyudmila Mihaylova;David Bull;Nishan Canagarajah
Affiliations:
University of Bristol, Department of Electrical and Electronic Engineering, BS8 1UB, Bristol, UK;Lancaster University, Department of Communication Systems, LA1 4WA, Lancaster, UK;University of Bristol, Department of Electrical and Electronic Engineering, BS8 1UB, Bristol, UK;University of Bristol, Department of Electrical and Electronic Engineering, BS8 1UB, Bristol, UK
Venue:
Machine Vision and Applications
Year:
2009

Citing 0
Cited 4

Optimised Particle Filter Approaches to Object Tracking in Video Sequences

IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
A spatio-spectral algorithm for robust and scalable object tracking in videos

ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Attenuated sequential importance resampling (A-SIR) algorithm for object tracking

ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking

Computers & Mathematics with Applications

Quantified Score

Hi-index	0.00

Visualization

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.