Deep residual network with subclass discriminant analysis for crowd behavior recognition
| Autor: | Paolo Remagnino, Vasileios Argyriou, Bappaditya Mandal, Dorothy Monekosso, Jiri Fajtl |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
QA75
Dynamic time warping Computer science business.industry Feature extraction 020206 networking & telecommunications Pattern recognition 02 engineering and technology Similarity measure Electronic mail Discriminative model 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Crowd psychology Classifier (UML) computer |
| Zdroj: | 2018 25th IEEE International Conference on Image Processing (ICIP) ICIP |
| ISSN: | 2381-8549 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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