A Rejection-Based Method for Event Detection in Video

This paper offers a natural extension of the newly introduced "anti-face" method to event detection, in both the gray-level and feature domains. For the gray-level domain, spatio-temporal templates are created by stacking the individual frames of the video sequence, 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 in the detection process. The following applications are presented: 1) detection of an object under three-dimensional (3-D) rotations in a video sequence simulated from the COIL database; 2) visual recognition of spoken words; and 3) recognition of two-dimensional and 3-D sketched curves. The technique is capable of detecting 3-D curves in viewing directions which substantially differ from those in the training set. The resulting detection algorithm is very fast and can successfully detect events even in very low resolution. Also, it is capable of discriminating the desired event from arbitrary events, and not only from those in a negative training set. Possible applications of the techniques offered in this paper are in man-machine interaction, surveillance, and search and summarization in video databases.