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# System prepended metadata

title: '(7/24)Computer Vision Recent Paper:Joint Monocular 3D Vehicle Detection and Tracking'
tags: [paper]

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# (7/24)Computer Vision Recent Paper:Joint Monocular 3D Vehicle Detection and Tracking
###### tags:`paper`

[toc]

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## Before Meeting
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### Author
- Hou-Ning Hu
- https://scholar.google.com/citations?user=VNSzxhUAAAAJ&hl=en
- ![](https://i.imgur.com/6GQIVbl.png)

- Qi-Zhi Cai
- https://scholar.google.com/citations?user=oyh-YNwAAAAJ&hl=en
- ![](https://i.imgur.com/Ep6qsnW.png)

- Min Sun
- https://scholar.google.com/citations?user=1Rf6sGcAAAAJ&hl=zh-TW
- ![](https://i.imgur.com/8CC4FWm.png)

- Dequan Wang
- https://scholar.google.com/citations?user=kFvxQ7YAAAAJ&hl=en
- ![](https://i.imgur.com/SC2giwq.png)

- Ji Lin
- https://scholar.google.com/citations?user=dVtzVVAAAAAJ&hl=en
- ![](https://i.imgur.com/O4s79UA.png)

- Trevor Darrell
- https://scholar.google.com/citations?user=bh-uRFMAAAAJ&hl=en
- ![](https://i.imgur.com/CV7h5nt.png)

- Fisher Yu
- https://scholar.google.com/citations?user=-XCiamcAAAAJ&hl=en
- ![](https://i.imgur.com/GMTAEfd.png)



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[refer](https://arxiv.org/pdf/1811.10742.pdf)
[refer]()
[refer]()


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## Recent Paper

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### Joint Monocular 3D Vehicle Detection and Tracking

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#### Abstracion
- 3D vehicle detection and tracking from a monocular camera requires detecting and associating vehicles, and estimating their locations and extents together
- Our approach leverages 3D pose estimation to learn 2D patch association overtime and uses temporal information from tracking to obtain stable 3D estimation
- Our method also leverages 3D box depth ordering and motion to link together the tracks of occluded objects
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#### Detail
- Introduction
    - perceive the 3D world in both space and time from simple sequences of 2D images rather than 3D point cloud
    - ![](https://i.imgur.com/0uFhDxK.png)
    - Good tracking helps 3D detection, as information along consecutive frames is integrated. Good 3D detection helps tracking, as ego-motion can be factored out.
    -  deep network architecture to track and detect vehicles jointly in 3D from a series of monocular color images
    -  After detecting 2D bounding box of targets, we utilize both world coordinates and re-projected camera coordinates to associate instances cross frames

- Related Works
    - Object tracking
    - Object detection
    - Driving datasets 
        - ![](https://i.imgur.com/NhgCpJ0.jpg)

- Joint 3D Detection and Tracking
    - Our goal is to track objects and infer their precise 3D location, orientation, and dimension from a single monocular video stream
    - We model 3D information with a layer-aggregating network on the object proposals.
    - We leverage estimated 3D information of current trajectories to track them through time, using 3D re-projection to generate similarity metric between all trajectories and detected boxes
    - Problem Formulation
        - ![](https://i.imgur.com/JdfvC0L.png)
        - convolutional network pipeline trained on very large amount of ground truth supervision
        - 
    - Candidate Box Detection
        - Faster R-CNN [35] trained on our dataset to provide bounding boxes of object proposals
        - 3D box center projection
            - To estimate 3D layout from single image more accurately, we extends the design of Region Proposal Network (RPN) to hypothesize a projected 2D point from 3D bounding box center
            - raw images are fed into a deep layeraggregated ConvNet to generate global convolutional feature maps.
    - 3D Box Estimation
    - Data Association and Tracking
        - Occlusion-aware Data Association.
            - ![](https://i.imgur.com/dSQG2dI.png)

        - Depth-Ordering Matching
    - Motion Model
        - Deep Motion Estimation and Update
- 3D Vehicle Tracking Simulation Dataset
- Experiments
    - 3D Estimation
    - Object Tracking
    - Overall Evaluation


- Implementation
    - Training
    - Dataset

- Results
    - 3D for Tracking
    - Tracking for 3D
    - Real-world Evaluation
    - Amount of Data Matters
    - 
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#### Conclusion
- In this paper, we learn 3D vehicle dynamics from monocular videos. We propose a novel framework, combining spatial visual feature learning and global 3D state estimation,to track moving vehicles in 3D world.
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[refer]()

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#### Abstracion
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#### Detail


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#### Conclusion

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[refer]()

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