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Unsupervised methods for abnormal event detection in video

Feb 12, 2018 · București, Romania

• What we'll do
We will present two frameworks for abnormal event detection in video that require no training sequences. The first framework [1] detects changes on a sequence of data from the video to see which frames are distinguishable from all the previous frames. As the authors want to build an approach independent of temporal ordering, they create shuffles of the data by permuting the frames before running each instance of the change detection. The second framework [2] is based on unmasking, a technique previously used for authorship verification in text documents, which we adapted to abnormal event detection as follows: a binary classifier is iteratively trained to distinguish between two consecutive video sequences while removing at each step the most discriminant features. Higher training accuracy rates of the intermediately obtained classifiers represent abnormal events.

[1] ECCV 2016: https://www.ri.cmu.edu/pub_files/2016/10/anomalyframework.pdf
[2] ICCV 2017: http://openaccess.thecvf.com/content_ICCV_2017/papers/Ionescu_Unmasking_the_Abnormal_ICCV_2017_paper.pdf

Speaker: Radu Ionescu

Location: Stoilow hall, 1st floor, Faculty of Mathematics and Computer Science

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  • Bucharest Deep Learning

    This is a group for anyone willing to learn and share ideas and expertise in Machine Learning. The members practice ML inside academia, industry or as a hobby. Our current focus is on Vision (deep learning network architectures), Visual Q&A, NLP and Reinforcement Learning. On medium term, we want to extend our topics to other ML areas such as Unsupervised Learning, Transfer Learning and Memory Networks.  During our meetups, presenters will share state-of-the-art papers and results, will present our own

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