Розпізнавання крадіжок у магазинах на записах камер відеоспостереження за допомогою комбінованої моделі CNN-RNN

Abstract

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.