# Multi-scale Region Proposal Network Trained by Multi-domain Learning for Visual Object Tracking

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/multi-scale-region-proposal-network-trained-by-multi-domain-learning-for-visual/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Yang Fang,Seunghyun Ko,Geun‐Sik Jo |
| Citations | 7 |
| DOI | 10.1007/978-3-319-70090-8_34 |
| Fields | Computer Science,Engineering |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W2766656136 |
| Type | conference-paper |
| Year | 2017 |

## Paper authors

- [Yang Fang](https://scholariq.org/researchers/yang-fang/)

## Paper journal

- [Lecture notes in computer science](https://scholariq.org/journals/lecture-notes-in-computer-science/)

## Paper primary topic

- [Video Surveillance and Tracking Methods](https://scholariq.org/topics/video-surveillance-and-tracking-methods/)

## Paper topics

- [Video Surveillance and Tracking Methods](https://scholariq.org/topics/video-surveillance-and-tracking-methods/)
- [Fire Detection and Safety Systems](https://scholariq.org/topics/fire-detection-and-safety-systems/)
- [IoT-based Smart Home Systems](https://scholariq.org/topics/iot-based-smart-home-systems/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.