Pedestrian detection by profile laser scanning

Detecting pedestrian flow parameters is of high priority in many future applications. Besides conventional solutions, such as special gates that allow only entering one-by-one or registering individuals by cards, novel remote sensing methods are able to support pedestrian sensing and detection. Mechanical solutions are expensive and slow down pedestrian traffic. Camera systems are able to collect sensitive information that require careful and complicated data handling and privacy management. Besides, acquiring 3D information (e.g. height of humans) is complicated and technical capabilities of optical sensors seriously decrease in case of dark or foggy/smoky environment. This paper discusses a method based on profile laser scanning technology and it gives a brief overview on the high accuracy and data rate laser scanning technology and on the primary application fields. In the proposed method, devices deployed in pedestrian corridors are able to continuously monitor and detect the traffic flow within the range of the sensor. The scanners are mounted in oblique position and scan perpendicular to the pedestrian traveling direction. In addition to the height and width of humans, their position and velocity also can be derived. Based on the collected data, useful information can be obtained for multiple purposes: directed pedestrian count/volume, traffic classification, flow distribution, velocity determination and additional information, e.g. on carried baggage. The proposed method does not involve collecting sensitive information about human beings, thus no privacy issues raised. The suggested technique is safe and has absolutely no hindering effect on the traffic flow, needs only info-communication support and power supply. The developed method has been validated by multiple field measurements. Reference data has been acquired by video that enabled the verification of the count, traveling direction and velocity estimation. The paper presents some statistics of the co- parative analysis that prove the accuracy and reliability of the data obtained.