Anomaly Detection for Security in Children's Play Areas Based on Image Using Multiple Lines Detection Method

This study aims to build a device as a security system to detect anomalies of children moving in play areas based on the Multiple Line Detection (MLD) method in a streaming image. We developed this device to help parents monitor their children's activities when playing in dangerous areas of the home to protect children from kidnapping. In this study, the MLD method can detect the children's activities when playing in three zones: the safe zone with green lines in the image, the caution zone with yellow lines, and the danger zone with red lines. The hardware used to build the devices in this study consists of three components: a camera to stream the image activities of children, a Raspberry Pi to process the image using OpenCV, and a buzzer for early security systems. The results of this study show that when the device detected the children playing in the safe zone, the system commanded the buzzer to turn off. Furthermore, when the camera detects that the children are playing in the caution and danger zone, the device then commands the buzzer to turn on as an early warning security system for the parents

Kidnapping Detection and Recognition in Previous Unknown Environment

An unaware event referred to as kidnapping makes the estimation result of localization incorrect. In a previous unknown environment, incorrect localization result causes incorrect mapping result in Simultaneous Localization and Mapping (SLAM) by kidnapping. In this situation, the explored area and unexplored area are divided to make the kidnapping recovery difficult. To provide sufficient information on kidnapping, a framework to judge whether kidnapping has occurred and to identify the type of kidnapping with filter-based SLAM is proposed. The framework is called double kidnapping detection and recognition (DKDR) by performing two checks before and after the “update” process with different metrics in real time. To explain one of the principles of DKDR, we describe a property of filter-based SLAM that corrects the mapping result of the environment using the current observations after the “update” process. Two classical filter-based SLAM algorithms, Extend Kalman Filter (EKF) SLAM and Particle Filter (PF) SLAM, are modified to show that DKDR can be simply and widely applied in existing filter-based SLAM algorithms. Furthermore, a technique to determine the adapted thresholds of metrics in real time without previous data is presented. Both simulated and experimental results demonstrate the validity and accuracy of the proposed method