Laser-based segment classification using a mixture of bag-of-words

By Jens Behley, Volker Steinhage, Armin Bernd Cremers. 2013

100 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Signal Processing

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

1. Raquel Urtasun
2. Bo Li
3. Xiaozhi Chen
4. Huimin Ma
5. Tian Xia
6. Antonio M. López
7. Silvio Savarese
8. Yaguo Lei

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Raquel Urtasun
3. Xiaogang Wang
4. Jitendra Malik
5. Ming-Hsuan Yang
6. Dacheng Tao
7. Trevor Darrell
8. Bernt Schiele

In Signal Processing

1. Bo Li
2. Jing Lin
3. Steven X. Ding
4. Silvio Savarese
5. Iro Armeni
6. Yaguo Lei
7. Yukun Zhu
8. Tian Xia

Citations in the corpus

• Vehicle Detection from 3D Lidar Using Fully Convolutional Network (2016, methodology): “Thus in this experiments, we only compare the proposed approach with range scan methods of Wang and Posner [31], Behley et al. [2], Plotkin [22].”
• Voting for Voting in Online Point Cloud Object Detection (2015, neutral): “[1] also take a segmentationbased approach.”
• Pedestrian detection combining RGB and dense LIDAR data (2014, background): “Conversely, approaches that use directly 3D points, as in [19], could explore objects size in a much more direct way but at the cost of designing proper processing stages such as:”
• An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data (2016, methodology): “In neural networks, softmax regression is often implemented at the final layer for multiclass classification [34].”
• 3D Semantic Parsing of Large-Scale Indoor Spaces (2016, methodology): “Baselines: We compare our method against the top performing algorithms from the KITTI object detection [11] dataset, mBOW [6] and Vote3D [37].”
• DepthCN: Vehicle detection using 3D-LIDAR and ConvNet (2017, methodology): “2) Vehicle Detection Evaluation: DepthCN was evaluated against mBoW [3] which is one of the most relevant methods and like ours operate directly on 3D-LIDAR’s range”
• Multiview random forest of local experts combining RGB and LIDAR data for pedestrian detection (2015, methodology): “In this work, we extend this method to learn multiple models, one for each 3D pose, and evaluate both the original single model approach and the multi model approach.”
• 3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection (2016, background): “Easy Moderate Hard Easy Moderate Hard Easy Moderate Hard mBoWy [51] 36.”
• A Survey on 3D Object Detection Methods for Autonomous Driving Applications (2019, unknown): “, a mixture of bag-of-words classifiers [12]).”