Multi-viewpoint patterns and occlusions handling using hybrid features for vehicle tracking
Journal
Proceedings - IEEE International Symposium on Circuits and Systems
Journal Volume
2021-May
Date Issued
2021
Author(s)
Wu C.-W
Abstract
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. ? 2021 IEEE
Subjects
Excluding overlapping part
Occlusion
Partial sections
Salient features
Vehicle tracking
Motion compensation
Object detection
Hybrid features
Including rule
Learning-based methods
Multi-viewpoints
Partial similarities
Saliency features
State of the art
Tracking algorithm
Hybrid vehicles
SDGs
Type
conference paper