@article{Krishna14:TVS,
    type = {article},
    key = {Krishna14:TVS},
    title = {Temporal Video Segmentation by Event Detection: A Novelty Detection Approach},
    author = {Mahesh Venkata Krishna and Paul Bodesheim and Marco Körner and Joachim Denzler},
    journal = {Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Applications (PRIA)},
    year = {2014},
    number = {2},
    pages = {243-255},
    volume = {24},
    abstract = {Temporal segmentation of videos into meaningful image sequences containing some particular activities is an interesting problem in computer vision. We present a novel algorithm to achieve this semantic video segmentation. The segmentation task is accomplished through event detection in a frame-by-frame processing setup. We propose using one-class classification (OCC) techniques to detect events that indicate a new segment, since they have been proved to be successful in object classification and they allow for unsupervised event detection in a natural way. Various OCC schemes have been tested and compared, and additionally, an approach based on the temporal self-similarity maps (TSSMs) is also presented. The testing was done on a challenging publicly available thermal video dataset. The results are promising and show the suitability of our approaches for the task of temporal video segmentation.},
    doi = {10.1134/S1054661814020114},
    publisher = {Pleiades Publishing Ltd.},
    url = {https://doi.org/10.1134/S1054661814020114},
}