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Deep Residual Network With Subclass Discriminant Analysis For Crowd Behavior Recognition

Mandal, Bappaditya; Fajtl, Jiri; Argyriou, Vasileios; Monekosso, Dorothy; Remagnino, Paolo

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Authors

Jiri Fajtl

Vasileios Argyriou

Dorothy Monekosso

Paolo Remagnino



Abstract

In this work, we extract rich representations of crowd behavior from video using a fine-tuned deep convolutional neural residual network. Using spatial partitioning trees we create subclasses within the feature maps from each of the crowd behavior attributes (classes). Features from these subclasses are then regularized using an eigen modeling scheme. This enables to model the variance appearing from the intra-subclass information. Low dimensional discriminative features are extracted after using the total subclass scatter information. Dynamic time warping is used on the cosine distance measure to find the similarity measure between videos. A 1-nearest neighbor (NN) classifier is used to find the respective crowd behavior attribute classes from the normal videos. Experimental results on large crowd behavior video database show the superior performance of our proposed framework as compared to the baseline and current state-of-the-art methodologies for the crowd behavior recognition task.

Conference Name 2018 IEEE International Conference on Image Processing
Conference Location Athens
Start Date Oct 7, 2018
End Date Oct 10, 2018
Acceptance Date May 4, 2018
Publication Date Sep 6, 2018
Publicly Available Date Mar 28, 2024
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Series Title IEEE International Conference on Image Processing
DOI https://doi.org/10.1109/ICIP.2018.8451190
Keywords crowd behavior recognition, feature extraction, discriminant analysis, residual network
Publisher URL https://doi.org/10.1109/ICIP.2018.8451190

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