People tracking with range cameras using density maps and 2D blob splitting

被引:5
|
作者
Van Crombrugge, Izaak [1 ]
Penne, Rudi [1 ,2 ]
Vanlanduit, Steve [1 ]
机构
[1] Univ Antwerp, Fac Appl Engn, Groenenborgerlaan 171, B-2020 Antwerp, Belgium
[2] Univ Antwerp, Dept Math, B-2020 Antwerp, Belgium
关键词
Human tracking; depth camera; time-of-flight; RGB-D; industrial safety; PEDESTRIAN DETECTION; CALIBRATION; ALGORITHM;
D O I
10.3233/ICA-190600
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A robust method is proposed for people tracking using range cameras: Density Map Tracking with Blob Splitting (DMT-BS). It was designed primarily for worker safety in industrial environments, but can be used in other applications as well. Multiple cameras can easily be added to resolve occlusions and to enlarge the observed area. The method could be used to track any moving object in an otherwise static environment as the detection does not rely on a specific human model. Its strength lies in its simplicity, making the behavior predictable and opening possibilities to be implemented on low-cost hardware. From the point cloud delivered by the depth sensor, a 2D density map is formed in floor coordinates followed by basic 2D-tracking. Robustness of this tracking is enhanced using a simple but effective blob splitting technique. Tests show that the camera position, depth noise, and extrinsic calibration errors have little influence on the tracker's performance. The proposed method was tested on three depth tracking datasets, reaching significantly better MOTA (Multiple Object Tracking Accuracy) scores when compared to two state-of-the-art depth-based trackers.
引用
收藏
页码:285 / 295
页数:11
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