Joint Monocular 3D Vehicle Detection and Tracking
By Hou-Ning Hu, Qi-Zhi Cai, Dequan Wang, Ji Lin, Min Sun, Philipp Krahenbuhl, Trevor Darrell, Fisher Yu. 2018
246 citations recorded by the source corpus.
Vehicle 3D extents and trajectories are critical cues for predicting the future location of vehicles and planning future agent ego-motion based on those predictions. In this paper, we propose a novel online framework for 3D vehicle detection and tracking from monocular videos. The framework can not only associate detections of vehicles in motion over time, but also estimate their complete 3D bounding box information from a sequence of 2D images captured on a moving platform. Our method leverages 3D box depth-ordering matching for robust instance association and utilizes 3D trajectory prediction for re-identification of occluded vehicles. We also design a motion learning module based on an LSTM for more accurate long-term motion extrapolation. Our experiments on simulation, KITTI, and Argoverse datasets show that our 3D tracking pipeline offers robust data association and tracking. On Argoverse, our image-based method is significantly better for tracking 3D vehicles within 30 meters than the LiDAR-centric baseline methods.
Subfields: Automotive Engineering, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
- Xingyi Zhou
- Dacheng Tao
- Jing Zhang
- Jianping Shi
- Shaoshuai Shi
- Philipp Krahenbuhl
- Xiaogang Wang
- Ping Luo
In Computer Vision and Pattern Recognition
- Dacheng Tao
- Luc Van Gool
- Xiaogang Wang
- Hongsheng Li
- Ming-Hsuan Yang
- Trevor Darrell
- Jitendra Malik
- Yi Yang
Citations in the corpus
- Objects as Points (2019, background): “Object detection powers many vision tasks like instance segmentation [7, 21, 32], pose estimation [3, 15, 39], tracking [24,27], and action recognition [5].”
- Learning Depth-Guided Convolutions for Monocular 3D Object Detection (2019, methodology): “Recent methods towards the above goal can be generally categorized into two streams as image-based approaches [36, 26, 41, 19, 17, 4] and pseudo-LiDAR point-based approaches [48, 33, 50].”
- 3D Object Detection for Autonomous Driving: A Comprehensive Survey (2022, methodology): “The proposed trackers include LSTM [100] and the 3D Kalman filter [14].”
- Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, background): “car [261], estimating the intention of cars and pedestrians and predicting their trajectories [244], [245].”