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Towards Real-Time Multi-Object Tracking

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JDE

Towards Real-Time Multi-Object Tracking

1. 训练步骤

以下操作步骤均以MOT16为例

1.1 准备数据集

  • 从MOT挑战赛官网下载数据集并解压
    wget https://motchallenge.net/data/MOT16.zip -P /data/tseng/dataset/jde
    cd /data/tseng/dataset/jde
    unzip MOT16.zip -d MOT16

  • 创建MOT16任务的工作区, 并将MOT格式标注文件转换为需要格式的标注文件
    git clone https://github.com/CnybTseng/JDE.git
    cd JDE
    mkdir -p workspace/mot16-2020-5-29
    ./tools/split_dataset.sh ./workspace/mot16-2020-5-29
    此时workspace/mot16-2020-5-29目录下会生成train.txt

1.2 从预训练模型导出参数生成JDE初始模型

  • 从darknet官网下载darknet53预训练模型
    wget https://pjreddie.com/media/files/darknet53.conv.74 -P ./workspace
    python darknet2pytorch.py -pm ./workspace/mot16-2020-5-29/jde.pth \
    --dataset ./workspace/mot16-2020-5-29 -dm ./workspace/darknet53.conv.74 -lbo
    此时workspace/mot16-2020-5-29目录下会生成初始模型jde.pth, 其骨干网已初始化为darnet53的参数

1.3 执行训练脚本

cp ./tools/train.sh ./train.sh
根据需要修改, 然后运行训练脚本
./train.sh

2. 测试

本项目实现了卡尔曼滤波的目标关联算法, 运行类似如下命令执行多目标跟踪
python tracker.py --img-path /data/tseng/dataset/jde/MOT16/test/MOT16-03/img1 \
--model workspace/mot16-2020-5-29/checkpoint/jde-ckpt-049.pth

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