Joint Deep Learning for Pedestrian Detection笔记
Posted Youngshuo
tags:
篇首语:本文由小常识网(cha138.com)小编为大家整理,主要介绍了Joint Deep Learning for Pedestrian Detection笔记相关的知识,希望对你有一定的参考价值。
1、结构图
Introduction
Feature extraction, deformation handling, occlusion handling, and classification are four important components in pedestrian detection. Existing methods learn or design these components either individually or sequentially. The interaction among these components is not yet well explored. This paper proposes that they should be jointly learned in order to maximize their strengths through cooperation. We formulate these four components into a joint deep learning framework and propose a new deep network architecture
Contribution Highlights
- A unified deep model for jointly learning feature extraction, a part deformation model, an occlusion model and classification. With the deep model, these components interact with each other in the learning process, which allows each component to maximize its strength when cooperating with others .
- We enrich the operation in deep models by incorporating the deformation layer into the convolutional neural networks (CNN). With this layer, various deformation handling approaches can be applied to our deep model.
- The features are learned from pixels through interaction with deformation and occlusion handling models . Such interaction helps to learn more discriminative features.
Citation
If you use our codes or dataset, please cite the following papers:
- W. Ouyang and X. Wang. Joint Deep Learning for Pedestrian Detection. In ICCV, 2013. PDF
Code (Matlab code on Wnidows OS)
Code and dataset on Google Drive:
For users who cannot download from Google Drive:
The files are on the GoogleDocs and Baidu. To Run the code, please read the following readme file:
- Readme
- 1. Put all of the documents into the same folder and decompress them using the command "extract to here". Suppose the root folder is "root", then you should have three folders "root/CNN", "root/data", "root/model", "root/NN", "root/tmptoolbox", "root/util", and "root/dbEval". For "root/data", there should be 4 folders: "root/data/CaltechTest", "root/data/CaltechTrain", "root/data/ETH", and "root/data/INRIATrain".
- 2. Run the "cnnexamples.m" or "testing.m." in the folder "root/CNN" to obtain the results.
FAQ
以上是关于Joint Deep Learning for Pedestrian Detection笔记的主要内容,如果未能解决你的问题,请参考以下文章
每日一读Joint Unsupervised Learning of Deep Representations and Image Clusters
每日一读Joint Unsupervised Learning of Deep Representations and Image Clusters
Joint Detection and Identification Feature Learning for Person Search
论文笔记-Joint Deep Modeling of Users and Items Using Reviews for Recommendation
On Joint Learning for Solving Placement and Routing in Chip Design
【Paper Reading】VideoBERT: A Joint Model for Videoand Language Representation Learning