ILASP can be used to detect events of interest in pre-processed streams of images. Pre-processing is necessary to convert unstructured image data into the more structured form, consisting of the coordinates of various objects, that can be used by ILASP. The pre-processing could be performed using off-the-shelf techniques.
In this example, we used the CAVIAR dataset (generated by the EC Funded CAVIAR project/IST 2001 37540), which has already been pre-processed and used by the Logic-based Machine Learning system OLED, which is specifically targeted at learning event definitions. We have previously shown that ILASP achieves a higher average F1 score on this dataset than OLED.
The CAVIAR dataset consists of video streams made up of images such as the one below. The image below has been pre-processed into a structured form containing the position and orientation of each person in the image, along with other information, such as whether they are walking, running, active or inactive, etc.
The images have been (manually) annotated to indicate when two people are meeting. We used ILASP to learn rules to predict when in the video stream these meeting events started and ended. For instance, one simple rule is that if two people are meeting and one stays still while the other is walking, then the meeting is terminated.