Steps Towards Improvements of Computer Vision Methods for Traffic Analysis
The rapid urbanization and increasing number of vehicles on the roads have stretched traditional traffic management systems to their limits. Intelligent Transportation Systems (ITS) offer a solution, utilizing advanced technologies to enhance traffic flow and safety. The robustness of computer vision methods within ITS, essential for traffic analysis, remains a crucial area for improvement. This thesis substantially contributes to this field, specifically focusing on Vehicle Fine-Grained Recognition, Vehicle Re-Identification, License Plate Recognition, and Monocular Vehicle Speed Measurement.
Several new datasets, highly appreciated by the research community, were introduced, enhancing the evaluation and exploration within each domain mentioned earlier. The main contributions can be summarized as follows:
- Novel method for aggregation of visual features for vehicle re-identification & dataset.
- Innovative approach to license plate recognition using alignment of the license plate and holistic recognition & three published datasets.
- Novel augmentation techniques for vehicle fine-grained recognition & extension of previously published dataset.
- The biggest dataset for vehicle speed measurement & baseline evaluation with state-of-the-art methods.
The key findings of this work demonstrate a significant enhancement in the accuracy, efficiency, and robustness of computer vision methods applied to traffic analysis.
This research's contributions have been recognized at top conferences and journals in ITS, setting new standards for future work. By advancing the current state of ITS and contributing valuable resources for ongoing research, this thesis represents a step towards more sustainable and efficient intelligent transportation systems.
traffic analysis, computer vision, vehicle fine-grained recognition, vehicle re-identification, vehicle speed measurement, license plate recognition, license plate alignment