Multi-Viewpoint Patterns and Occlusions Handling Using Hybrid Features for Vehicle Tracking

A novel vehicle tracking algorithm robust to multi- viewpoint pattern and occlusion is proposed. To improve accuracy, after object detection, the overlapping parts of bounding boxes are removed before block matching. It is helpful for reducing the interference from nearby vehicles or objects. To perform block matching well, in addition to partial similarity and position correlation, many advanced features, including saliency features and several new global features, are adopted. These features can retrieve important information from vehicles and are helpful for tracking. Experiments show that the proposed algorithm achieves favorable performance against state-of-the-art vehicle tracking methods, including rule-based and learning-based methods.