Abstract: Despite the advancement of information and transportation systems, inconvenient pedestrian crossing buttons remain common. In accordance with intelligent transportation systems (ITS), it is necessary to improve pedestrian crossing systems. Therefore, in this study, the proposed system adopts signal gaze, which is more natural compared to pressing a pedestrian crossing button, as a crossing request. A compact camera is inserted in a traffic light to view the other side of the crosswalk. The image data is analyzed in real time to identify all people who have a crossing request. An algorithm with three detectors using Haar-like feature quantities was developed and an evaluation experiment was conducted, considering three conditions: daytime, nighttime, and shade. The detection rate of crossing requests was 100% within 5 s. Although the detection rate was extremely high, there was a problem of incorrectly detecting non-humans. Therefore, in this research, we evaluated the system when detecting non-humans in order to determine the causes. As a result, it became clear that the detection rate changes rapidly depending on the waiting time for a traffic light and also when crossing the crosswalk; however, the system continues to detect the incorrectly detected background.