In recent years, various indoor social security emergencies have brought major changes to people's lives. However, the state-of-the-art surveillance technology achieving object tracking alone is insufficient through a single visual tracking method and may cause inaccurate tracking (such as burred faces, loss target, and occlusion) and is time-consuming for a long video sequence. Accurate tracking and localization systems are essential for developing indoor localization systems. This article designs a fusing communication system called the Internet of Electronic-Visual Things (IoEVT) localization system, which simultaneously tracks targets assisting with an adaptive Kalman filter (AKF). The data fusion indoor localization approach using AKF fuses multidata from electronic ( E) and visual ( V) data, such as WiFi and cameras. The fusing method significantly improves EV data sensing abilities for solving occluded visual information when only using single V data in occlusion scenarios and reduces the localization error rate. In addition, our real-world experimental validation shows that the proposed IoEVT system achieves better accuracy, scalability, and robustness with low network bandwidth consumption compared with the single E or V data in many scenarios, such as occlusion and occlusion-free.