Active Shift Attention Based Object Tracking System

Abstract

The human vision system (HVS) collects a huge amount of information and performs a variety of biological mechanisms to select relevant information. Computational models based on these biological mechanisms are used in machine vision to select interesting or salient regions in the images for application in scene analysis, object detection and object tracking.

Different object tracking techniques have been proposed often using complex processing methods. On the other hand, attention-based computational models have shown significant performance advantages in various applications. We hypothesise the integration of a visual attention model with object tracking can be effective in increasing the performance by reducing the detection complexity in challenging environments such as illumination change, occlusion, and camera moving.

The overall objective of this thesis is to develop a visual saliency based object tracker that alternates between targets using a measure of current uncertainty derived from a Kalman filter. This thesis presents the results by showing the effectiveness of the tracker using the mean square error when compared to a tracker without the uncertainty mechanism.

Specific colour spaces can contribute to the identification of salient regions. The investigation is done between the non-uniform red, green and blue (RGB) derived opponencies with the hue, saturation and value (HSV) colour space using video information. The main motivation for this particular comparison is to improve the quality of saliency detection in challenging situations such as lighting changes. Precision-Recall curves are used to compare the colour spaces using pyramidal and non-pyramidal saliency models.