Multi-Target Tracking With Multiple 2D Range Scanners

Abstract

We propose a novel efficient online method for tracking performers on stage. Most existing tracking methods have focused on using expensive, high-performing sensors, such as multilayer lidar sensors or high-resolution, short-range radars. Image sensor-based methods are not appropriate for tracking performers on stage because of challenging illumination conditions caused by lighting effects. In this paper, we introduce a robust multi-target tracking method based on the sensor fusing of two-dimensional distance sensors such as single-layer lidar sensors that have a relatively lower cost than the aforementioned types of sensors. In our method, measurements from each sensor are transformed into a reference coordinate system and objects are detected with those transformed measurements. Then, the object detections are used in generating or extending the trajectories of targets by detection-to-trajectory matching. In our experiments, we quantitatively evaluated the proposed method with a newly constructed dataset which consists of two scenarios simulating performances on stage. We collected the scan results of two single-layer lidar sensors and image frames captured by a camera sensor for each scenario. The experimental results show that the proposed method robustly tracks performers in challenging scenarios, in which the performers move abruptly and are densely located.

Keywords

• Sensor fusion
• multi-target tracking
• moving object detection