Recognition of Shoplifting Activities in CCTV Footage Using the Combined CNN-RNN Model
Ładowanie...
Data
2023
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Wydawnictwo Politechniki Łódzkiej
Lodz University of Technology Press
Lodz University of Technology Press
Abstrakt
The recognition of human activities through surveillance has numerous
applications across various fields. This article presents a proposed
approach to identify shoplifting in camera-recorded video data using a neural
classifier that combines two neural networks, specifically, convolutional
and recurrent networks. The hybrid architecture consists of two parallel
streams: initial and processed video fragments (histogram of oriented gradients
and optical flow). The convolutional network extracts features from
each frame of the video fragment, while the recurrent network processes the
temporal information from sequences of frames as features to classify the
activity.
Opis
Słowa kluczowe
human activity recognition, surveillance, shoplifting, convolutional neural network, recurrent neural network, features extraction, histogram of oriented gradients, optical flow, rozpoznawanie działalności człowieka, inwigilacja, kradzieże w sklepach, plotowa sieć neuronowa, rekurencyjna sieć neuronowa, ekstrakcja cech, histogram zorientowanych gradientów, przepływ optyczny
Cytowanie
Kirichenko L., Pichugina O., Sydorenko B., Yakovlev S., Recognition of Shoplifting Activities in CCTV Footage Using the Combined CNN-RNN Model. W: Progress in Polish Artificial Intelligence Research 4, Wojciechowski A. (Ed.), Lipiński P. (Ed.)., Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, s. 61-66, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.8.