Recognition of Shoplifting Activities in CCTV Footage Using the Combined CNN-RNN Model - Kirichenko, Lyudmyla; Pichugina, Oksana; Sydorenko, Bohdan; Yakovlev, Sergiy (Wydawnictwo Politechniki Łódzkiej, 2023)
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.
Keywords: 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