High-Utility Semantic Trajectory Pattern Mining for Security System

Abstract

In security systems，transforming a large number of collected target trajectories into semantic trajectories and mining their frequency patterns are helpful in analyzing target behavior patterns，identifying hazard sources，and enhancing internal prevention and control of security systems. Existing frequent-pattern mining methods do not consider the utility difference in stay-point values.To address this issue，this study proposes a high-utility semantic trajectory pattern mining method.The concept of semantic trajectory utility value is defined by integrating three parameters：the interest of the stay point，stay time of the target at stay point，and the support of target semantic trajectory.To achieve this，an ant colony algorithm is used to mine high-utility semantic trajectory patterns.The algorithm involves the elite ant strategy to improve the iterative method of ant population and the strategy of the ant selecting the next node through the roulette selection method. Next，the invalid coding vector-pruning strategy is used to improve the execution efficiency of the algorithm. The proposed algorithm is tested on four public datasets，namely Chess，Mushroom，Foodmart，and Retail，as well as the Radio Frequency IDentification（RFID） location dataset of a certain security system.The experimental results show that the proposed algorithm increases the number of high-utility semantic trajectory patterns by 10%-15% and reduces the running time by 7%-12% compared with the Ant Colony Optimization（ACO）-based approach to mine high-utility itemsets（HUIM-ACS）.