Anti-sequences: Event detection by frame stacking

This paper presents a natural extension of the newly introduced "anti-face" method to event detection, both in the image and in the feature domains. In the case of the image domain (video sequences) we create spatio-temporal templates by stacking the video frames, and the detection is performed on these templates. In order to recognize the motion of features in a video sequence, the spatial locations of the features are modulated in time, thus creating a one-dimensional vector which represents the event. The following applications of anti-sequences are presented: 1) Detection of an object under 3D rotations in a video sequence simulated from the COIL database, 2) Visual speech recognition of spoken words, and 3) Recognition of symbols sketched with a laser pointer. The resulting detection algorithm is very fast, and is robust enough to work on small images. Also, it is capable of discriminating the desired event-template from arbitrary events, and not only events in a "negative training set".