Int J Neural Syst - Multi-instance dictionary learning for detecting abnormal events in surveillance videos.

Tópicos

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Resumo

In this paper, a novel method termed Multi-Instance Dictionary Learning (MIDL) is presented for detecting abnormal events in crowded video scenes. With respect to multi-instance learning, each event (video clip) in videos is modeled as a bag containing several sub-events (local observations); while each sub-event is regarded as an instance. The MIDL jointly learns a dictionary for sparse representations of sub-events (instances) and multi-instance classifiers for classifying events into normal or abnormal. We further adopt three different multi-instance models, yielding the Max-Pooling-based MIDL (MP-MIDL), Instance-based MIDL (Inst-MIDL) and Bag-based MIDL (Bag-MIDL), for detecting both global and local abnormalities. The MP-MIDL classifies observed events by using bag features extracted via max-pooling over sparse representations. The Inst-MIDL and Bag-MIDL classify observed events by the predicted values of corresponding instances. The proposed MIDL is evaluated and compared with the state-of-the-art methods for abnormal event detection on the UMN (for global abnormalities) and the UCSD (for local abnormalities) datasets and results show that the proposed MP-MIDL and Bag-MIDL achieve either comparable or improved detection performances. The proposed MIDL method is also compared with other multi-instance learning methods on the task and superior results are obtained by the MP-MIDL scheme.

Resumo Limpo

paper novel method term multiinst dictionari learn midl present detect abnorm event crowd video scene respect multiinst learn event video clip video model bag contain sever subev local observ subev regard instanc midl joint learn dictionari spars represent subev instanc multiinst classifi classifi event normal abnorm adopt three differ multiinst model yield maxpoolingbas midl mpmidl instancebas midl instmidl bagbas midl bagmidl detect global local abnorm mpmidl classifi observ event use bag featur extract via maxpool spars represent instmidl bagmidl classifi observ event predict valu correspond instanc propos midl evalu compar stateoftheart method abnorm event detect umn global abnorm ucsd local abnorm dataset result show propos mpmidl bagmidl achiev either compar improv detect perform propos midl method also compar multiinst learn method task superior result obtain mpmidl scheme

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