Classification of different vehicles in traffic using RGB and Depth images: A Fast RCNN Approach
Mohan Kashyap Pargi, Bondan Setiawan, Yoriko Kazama · 2019
The Fast RCNN framework utilizes the region proposals generated from the RGB images in general for object classification and detection. This paper describes about the vehicle classification employing the Fast RCNN framework and utilizing the information provided from the combination of depth images and RGB images in the form of region proposals for object detection and classification. We use this underlying system architecture to perform evaluation on the Indian and Thailand vehicle traffic datasets. Overall, we achieve a mAP of 72.91% using RGB region proposals, and mAP of 73.77% using RGB combined with depth proposals, for the Indian dataset; and mAP of 80.61% on RGB region proposals, and mAP of 81.25% on RGB combined with depth region proposals, for the Thailand dataset. Our results show that RGB combined with depth region proposals mAP performance is slightly better than the region proposals generated using RGB images only. Furthermore, we provide insights on the performance of AP(Average Precision) for each vehicle on Thailand dataset and how effective region proposals generation is crucial for object detection using the FastRCNN framework.