Detecting Darting Out Pedestrians With Occlusion Aware Sensor Fusion of Radar and Stereo Camera

被引:16
作者
Palffy, Andras [1 ]
Kooij, Julian F. P. [1 ]
Gavrila, Dariu M. [1 ]
机构
[1] Delft Univ Technol, Intelligent Vehicles Grp, NL-2628 CD Delft, Netherlands
来源
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES | 2023年 / 8卷 / 02期
关键词
Radar; Cameras; Radar detection; Radar tracking; Automobiles; Laser radar; Intelligent vehicles; Advanced driver assistance systems; millimeter wave radar; object detection; radar detection; AUTOMOTIVE RADAR; TRACKING; VISION;
D O I
10.1109/TIV.2022.3220435
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Early and accurate detection of crossing pedestrians is crucial in automated driving in order to perform timely emergency manoeuvres. However, this is a difficult task in urban scenarios where pedestrians are often occluded (not visible) behind objects, e.g., other parked vehicles. We propose an occlusion aware fusion of stereo camera and radar sensors to address scenarios with crossing pedestrians behind such parked vehicles. Our proposed method adapts both the expected rate and properties of detections in different areas according to the visibility of the sensors. In our experiments on a real-world dataset, we show that the proposed occlusion aware fusion of radar and stereo camera detects the crossing pedestrians on average 0.26 seconds earlier than using the camera alone, and 0.15 seconds earlier than fusing the sensors without occlusion information. Our dataset containing 501 relevant recordings of pedestrians behind vehicles will be publicly available on our website for non-commercial, scientific use.
引用
收藏
页码:1459 / 1472
页数:14
相关论文
共 61 条
[1]  
Almeida A, 2005, ISIE 2005: PROCEEDINGS OF THE IEEE INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS 2005, VOLS 1- 4, P1327
[2]   Practical classification of different moving targets using automotive radar and deep neural networks [J].
Angelov, Aleksandar ;
Robertson, Andrew ;
Murray-Smith, Roderick ;
Fioranelli, Francesco .
IET RADAR SONAR AND NAVIGATION, 2018, 12 (10) :1082-1089
[3]  
[Anonymous], 2009, GLOB STAT REP ROAD S
[4]  
Badino H, 2009, LECT NOTES COMPUT SC, V5748, P51, DOI 10.1007/978-3-642-03798-6_6
[5]   Radar Transformer: An Object Classification Network Based on 4D MMW Imaging Radar [J].
Bai, Jie ;
Zheng, Lianqing ;
Li, Sen ;
Tan, Bin ;
Chen, Sihan ;
Huang, Libo .
SENSORS, 2021, 21 (11)
[6]  
Barnes D, 2020, IEEE INT CONF ROBOT, P6433, DOI [10.1109/ICRA40945.2020.9196884, 10.1109/icra40945.2020.9196884]
[7]  
Bartels B., 2014, FAT SCHRIFTENREIHE, V268, P1
[8]   Pedestrian recognition using automotive radar sensors [J].
Bartsch, A. ;
Fitzek, E. ;
Rasshofer, R. H. .
ADVANCES IN RADIO SCIENCE, 2012, 10 :45-55
[9]   Simple Pair Pose - Pairwise Human Pose Estimation in Dense Urban Traffic Scenes [J].
Braun, Markus ;
Flohr, Fabian B. ;
Krebs, Sebastian ;
Kressel, Ulrich ;
Gavrila, Dariu M. .
2021 32ND IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), 2021, :1545-1552
[10]   EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes [J].
Braun, Markus ;
Krebs, Sebastian ;
Flohr, Fabian ;
Gavrila, Dariu M. .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2019, 41 (08) :1844-1861