Vehicle Instance Segmentation Polygonal Dataset for a Private Surveillance System

Vehicle identification and re-identification is an essential tool for traffic surveillance. However, with cameras at every corner of the street, there is a requirement for private surveillance. Automated surveillance can be achieved through computer vision tasks such as segmentation of the vehicle, classification of the make and model of the vehicle and license plate detection. To achieve a unique representation of every vehicle on the road with just the region of interest extracted, instance segmentation is applied. With the frontal part of the vehicle segmented for privacy, the vehicle make is identified along with the license plate. To achieve this, a dataset is annotated with a polygonal bounding box of its frontal region and license plate localization. State-of-the-art methods, maskRCNN, is utilized to identify the best performing model. Further, data augmentation using multiple techniques is evaluated for better generalization of the dataset. The results showed improved classification as well as a high mAP for the dataset when compared to previous approaches on the same dataset. A classification accuracy of 99.2% was obtained and segmentation was achieved with a high mAP of 99.67%. Data augmentation approaches were employed to balance and generalize the dataset of which the mosaic-tiled approach produced higher accuracy.

Automated summary

Vehicle identification and re-identification is crucial for traffic surveillance. Computer vision techniques like vehicle segmentation, make and model classification, and license plate detection enable automated surveillance. Instance segmentation is used to uniquely represent each vehicle on the road by extracting the region of interest. By segmenting the frontal part of the vehicle, the make and license plate can be identified. The study utilizes the state-of-the-art maskRCNN method to achieve the best performing model and evaluates data augmentation techniques for improved dataset generalization. The results show significant improvements in classification accuracy and mAP compared to previous approaches.