40 lines
2.5 KiB
Markdown
40 lines
2.5 KiB
Markdown
# Towards-Realtime-MOT
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**NOTE:** Still in progress, will update constantly, thank you for your attention!
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## Introduction
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This repo is the a codebase of the Joint Detection and Embedding (JDE) model. JDE is a fast and high-performance multiple-object tracker that learns the object detection task and appearance embedding task simutaneously in a shared neural network. Techical details are described in our [arXiv preprint paper](https://arxiv.org/pdf/1909.12605v1.pdf). By using this repo, you can simply achieve **MOTA 64%+** on the "private" protocol of [MOT-16 challenge](https://motchallenge.net/tracker/JDE), and with a near real-time speed at **18~24 FPS** (Note this speed is for the entire system, including the detection step! ) .
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We hope this repo will help researches/engineers to develop more practical MOT systems. For algorithm development, we provide training data, baseline models and evaluation methods to make a level playground. For application usage, we also provide a small video demo that takes raw videos as input without any bells and whistles.
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## Requirements
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* Python 3.6
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* [Pytorch](https://pytorch.org) >= 1.0.1
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* [syncbn](https://github.com/ytoon/Synchronized-BatchNorm-PyTorch) (Optional, compile and place it under utils/syncbn, or simply replace with nn.BatchNorm [here](https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/models.py#L12))
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* [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) (Their GPU NMS is used in this project)
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* python-opencv
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* ffmpeg (Optional, used in the video demo)
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## Video Demo
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<img src="assets/MOT16-03.gif" width="400"/> <img src="assets/MOT16-14.gif" width="400"/>
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<img src="assets/IMG_0055.gif" width="400"/> <img src="assets/000011-00001.gif" width="400"/>
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Usage:
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```
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python demo.py --input-video path/to/your/input/video --weights path/to/model/weights
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--output-format video --output-root path/to/output/root
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```
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## Dataset zoo
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Will be released later.
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## Pretrained model and baseline models
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Darknet-53 ImageNet pretrained: [[DarkNet Official]](https://pjreddie.com/media/files/darknet53.conv.74)
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JDE-1088x608-uncertainty: [[Google Drive]]()(Coming soon) [[Baidu NetDisk]](https://pan.baidu.com/s/1Ifgn0Y_JZE65_qSrQM2l-Q)
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## Test on MOT-16 Challenge
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## Training
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## Train with custom datasets
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## Acknowledgement
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A large portion of code is borrowed from [ultralytics/yolov3](https://github.com/ultralytics/yolov3) and [longcw/MOTDT](https://github.com/longcw/MOTDT), many thanks to their wonderful work!
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