About The Protocol
This protocol uses the real world PKLot [1] problem as a concept drift benchmark.
Protocol Definition
- The problem is defined as classifying each individual parking space as vacant or occupied.
- The LBP uniform [2] is defined as the feature set.
- Days containing less than 50 samples for each class (vacant or occupied) from the original dataset are not considered. These days were already removed in this version of the benchmark.
- The parking lots are presented in the order UFPR04, UFPR05 and PUCPR. The images collected in each parking lot are ordered in the chronological order and each day represent a time step. Thus, the time steps are: Day1_UFPR04, … , Last_UFPR04, Day1_UFPR05, … , Last_UFPR05, Day1_PUCPR, … , Last_PUCPR. This configuration generates a camera position change (UFPR04 to UFPR05) and then a parking lot change (UFPR05 to PUCPR).
- At each time step, all instances of the current day must be classified. Also, at each time step (day) 50 samples from each class from the previous day are randomly selected for training. This configuration simulates a scenario where a human supervisor may label a small batch to update the classification system.
Download
You may download the files containing the LBP uniform features already extracted and ordered according to the proposed protocol here.
When you extract the tar.gz file, you will find several directories. The numbers in parenthesis in each directory represent the order that the data should be fed. First, the days in the UFPR04, then UFPR05 and Finally PUC. Each day is presented as a directory, named by a number in parenthesis representing its order, and the day that it was collected in the format YYYY-MM-DD. Inside the days folder, you can find the LBP Uniform features extracted from each individual parking space, in the Weka/MOA (arff) format.
References
- [1] Almeida, P., Oliveira, L. S., Britto Jr, A., Sabourin, R., Handling Concept Drifts Using Dynamic Selection of Classifiers. IEEE International Conference on Tools with Artificial Intelligence, San Jose, USA, 2016. See the publication.
- [2] Ojala, T., Pietikainen, M., & Maenpaa, T. (2002, Jul). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971-987. doi: 10.1109/TPAMI.2002.1017623
License
This database is licensed under a Creative Commons Attribution 4.0 International License.