Color feature integration with directional ringlet intensity feature transform for enhanced object tracking

Object tracking, both in wide area motion imagery (WAMI) and in general use cases, is often subject to many different challenges, such as illumination changes, background variation, rotation, scaling, and object occlusions. As WAMI datasets become more common, so too do color WAMI datasets. When color data is present, it can offer very strong potential features to enhance the capabilities of an object tracker. A novel color histogram-based feature descriptor is proposed in this thesis research to improve the accuracy of object tracking in challenging sequences where color data is available. The use of a three dimensional color histogram is explored, and various color spaces are tested. It is found to be effective but overly costly in terms of calculation time when comparing reference features to the test features. A reduced, two dimensional histogram is proposed, created from three channel color spaces by removing the intensity/luminosity channel before calculating the histogram. The two dimensional histogram is also evaluated as a feature for object tracking, and it is found that the HSV two dimensional histogram performs significantly better than other color space histograms, and that the two dimensional histogram performs at a level very near that of the three dimensional histogram, but an order of magnitude less complex in the feature distance calculation. The proposed color feature descriptor is then integrated with the Directional Ringlet Intensity Feature Transform (DRIFT) object tracker. The two dimensional HSV color histogram is enhanced further by making use of the DRIFT Gaussian ringlets as a mask for the histogram, resulting in a set of weighted histograms as the color feature descriptor. This is calculated alongside the existing DRIFT features of intensity and Kirsch mask edge detection. The distance scores for the color feature and DRIFT features are calculated separately, given the same weight, and then added together to form the final hybrid feature distance score. The combined proposed object tracker, C-DRIFT, is evaluated on both challenging WAMI data sequences and challenging general case tracking sequences that include head, body, object, and vehicle tracking. The evaluation results show that the proposed C-DRIFT algorithm significantly improves on the average accuracy of the DRIFT algorithm. Future work on the integrated algorithm includes integrated scale change handling created from a hybrid of normalized color histograms and existing DRIFT rescaling methods