This dataset, called SSIG-ALPR Database, was created to help researchers evaluating automatic license plate recognition problems. The data for the paper Real-time Automatic License Plate Recognition Through Deep Multi-Task Networks (link to the research page) were captured during the day using two cameras: one placed static while recording the vehicles that were passing by and another placed within a vehicle that recorded while the vehicle moved accross the city.… Read more
The experimental results for the paper Assigning Relative Importance to Scene Elements in SIBGRAPI’2017 (link to the research page) were obtained using two datasets: VIP dataset and UIUC Pascal Sentence. Both datasets are associate to importance assignment researches and present a wide range of images containing multiple objects per image.
In order to use both datasets on the paper Assigning Relative Importance to Scene Elements, it was necessary to generate importance annotations, since they are not provided along dataset images.… Read more
This dataset, called SSIG SegPlate Database, aims at evaluating the License Plate Character Segmentation (LPCS) problem. The experimental results for the paper Benchmark for License Plate Character Segmentation (link to the research page) were obtained using a dataset providing 101 on-track vehicles captured during the day. The video was recorded using a static camera in the early 2015.… Read more
The experimental results for the paper Learning Discriminative Appearance-Based Models Using Partial Least Squares in SIBGRAPI’2009 (link to the research page) were obtained using the ETHZ dataset, which provides a large number of different people captured in uncontrolled conditions. The video sequences are captured from moving cameras, which provides a range of variations in people’s appearances.
We used the ground truth location of people in the video to crop each person, then we created a directory containing samples of each person (p0??… Read more