Multi Feature Rgb D Generic Object Tracking Using A Simple Filter Hierarchy PDF Download

Visual object tracking is an important research area within computer vision. Object tracking has applications in security, surveillance, robotics, and safety systems. In generic single object tracking, the problem is constrained to short-term tracking where the target is initialized using its location in a single frame and the tracker is not reinitialized. This is challenging because trackers must update the target model using predicted targets in later frames. However, this has a large potential to cause model drift as errors are introduced over time. Additional challenges that are present in visual tracking include illumination changes, partial and full occlusions, deformation of the target, viewpoints changes, scale change, complex backgrounds and clutter, and similar objects in the scene. A widely used strategy for improved tracking is to combine various complementary features. Combination strategies are varied in how they use the multiple features or trackers. Adaptive fusion is performed by basing the weighting on the value of individual estimates in previous frames. The proposed tracking scheme takes inspiration from human vision to reduce the risk of tracking errors. In our proposed tracking scheme, the learned adaptive feature fusion (LAFF) method, a robust modular tracker is created by adaptability updating the weighting scheme based on a trained system for scoring each estimator. This is accomplished by first researching previous feature fusion techniques and examining their shortcomings. A variance ratio based method for adaptive feature fusion (AFF) is developed and evaluated. Next, a machine learning based method is created to help further improve robustness for the tracker. The LAFF method is an extension of AFF that teaches a machine learned regressor to generate fusion weights for a set of features. A suite of diverse features is selected for fast and accurate tracking, while also demonstrating the advantage of adaptive fusion. These features are improved to introduce more diversity into the target model. Additional tracking components are developed to overcome specific track challenges and to increase the overall robustness of the tracker. These improvements include work on search area selection, occlusion handling, and target scale change. A motion tracker is also developed to interact in parallel to the feature tracker. The two main goals of the proposed tracker are to be a robust tracker and a modular multi-estimate tracker. The robustness indicates that the tracker can overcome typical challenges that are present in the data. The tracker should also be robust to the target selection, meaning the boundary should not be expected to be perfect. A modular multi-feature tracker implies that the tracker is made up of multiple feature types and that these can be user selected based on need. It also means that new features or trackers can be incorporated easily into the existing frame and the tracker will automatically adjust to best utilize the new features. The features can be limited for performance on a certain operating platform or expanded to achieve higher accuracy. The LAFF tracker is evaluated on four diverse datasets against a set of competitive single and multi-estimate trackers.