On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts

By Alejandro González, David Vázquez, Antonio M. López, Jaume Amores. 2017

103 citations recorded by the source corpus.

Subfields: 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 Computer Vision and Pattern Recognition

1. Xiaogang Wang
2. Raquel Urtasun
3. Luc Van Gool
4. Hongsheng Li
5. Jiashi Feng
6. Dacheng Tao
7. Jiaya Jia
8. Bo Li

Citations in the corpus

• VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection (2017, neutral): “There are also several multi-modal fusion methods that combine images and LiDAR to improve detection accuracy [10, 16, 5].”
• Multi-view 3D Object Detection Network for Autonomous Driving (2016, methodology): “[9, 6] combine LIDAR and images for 2D detection by employing early or late fusion schemes.”
• Optimal Sensor Data Fusion Architecture for Object Detection in Adverse Weather Conditions (2018, methodology): “There are various examples in the literature for either fusing the sensor data at the beginning (Early Fusion) [6], [7], [14], or at the end of the fusion process (Late Fusion) [2], [12] so that it is difficult to decide, which sensor fusion approach is suited, especially since the object detectors ”
• Autonomous Cars: Research Results, Issues, and Future Challenges (2019, background): “Recent developments in sensor fusion [48], [49] are encouraging and could be incorporated into the commercial autonomous car technology.”
• Scale-Aware Fast R-CNN for Pedestrian Detection (2015, methodology): “Hand-crafted Model: The models based on hand-crafted features have been widely used for object detection [2], [5], [8]–[10], [13], [18], [19], [30], [39], [51], [54], [61], [62].”
• PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition (2018, methodology): “In the context of autonomous driving, images, depth and optical flow are fused together by a mixture-of-experts framework for 2D pedestrian detection [9].”
• Joint 3D Proposal Generation and Object Detection from View Aggregation (2017, background): “Multimodal object detectors have been previously discussed in the literature [12, 13, 14, 15, 3, 7], but up to our knowledge, no previous work has exploited features from both point cloud and RGB data to generate region proposals.”
• Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, background): “The combination of information from different sensors via sensor fusion (Enzweiler & Gavrila (2011); Chen et al. (2016b); González et al. (2016)) allows for the robust integration of this complementary information.”