Optimized Parameter Tuning in a Recurrent Learning Process for Shoplifting Activity Classification

Abstract

Abstract From recent past, shoplifting has become a serious concern for business in both small/big shops and stores. It customarily involves the buyer concealing store items inside clothes/bags and then leaving the store without payment. Unfortunately, no cost-effective solution is available to overcome this problem. We, therefore intend to build an expert monitoring system to automatically recognize shoplifting events in megastores/shops by recognizing object-stealing actions of humans. The method proposed utilizes a deep convolutional-based InceptionV3 architecture to mine the prominent features from video clips. These features are used to custom Long Short Term Memory (LSTM) network to discriminate human stealing actions in video sequences. Optimizing recurrent learning classifier using different modeling parameters such as sequence length and batch size is a genuine contribution of this work. The experiments demonstrate that the system proposed has achieved an accuracy of 89.36% on the synthesized dataset, which comparatively outperforms other existing methods.