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baixb cccd9e23f2 | 2 years ago | |
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.META-INF | 2 years ago | |
.idea | 2 years ago | |
AnoDetector | 2 years ago | |
AnoVideos/RoadAccidents002_x264 | 2 years ago | |
I3D | 2 years ago | |
__pycache__ | 2 years ago | |
RADetector.py | 2 years ago | |
README.md | 2 years ago | |
config.yaml | 2 years ago | |
test.py | 2 years ago |
RoadAccidentDetector
杨泽华
检测视频中交通事故及其所在视频段
资源占用:
GPU内存使用:2~3G
准确率:79.4% (URAD数据集测试结果)
检测速度:43 FPS
AUC、FPS
UCF_Crimes, URAD, CADP
类别 | 名称 | 版本 |
---|---|---|
os | Ubuntu or Windows | 16.04(Ubuntu); Win10(Windows) |
编程语言 | python | 3.7 |
深度学习框架 | pytorch | 1.5.1 |
深度学习框架 | torchvisioin | 0.6.1 |
计算机视觉框架 | opencv-contrib-python | 4.2.0.34 |
图像处理库 | pillow | 7.1.2 |
数据处理 | numpy | 1.18.5 |
过程可视化 | tqdm | 4.46.1 |
代码的输入与输出。如下所示:
名称 | 说明 |
---|---|
输入 | 视频帧(像素尺寸无限制) |
输出 | 长度32的numpy array,分别代表视频的32个段的预测异常分数 |
在terminal下运行以下命令:
$ cd project_dir
$ python RADetector.py [-h] [--config_path PATH_TO_YAML_CONFIG_FILE]
参数说明:
config_path: 包含检测参数的.yaml文件
其中需包含:
gpu: 使用的GPU号
read_mode: 数据输入的方式,其中
'vdir':根据路径读取视频帧,需要给出vpath参数来指定目标视频的路径
'frames':直接传递视频帧,类型为包含视频帧的数组,具体见类注释。使用该模式需要直接对RADetector类的示例调用__call__(frames)方法传入参数
use_merge: True or False 指定是否同时使用RGB和FLOW模式的分数进行融合得到最终的异常分数
use_norm: True or False 指定是否使用最大最小归一化
vpath: str 指定输入视频路径
rgb_mdir: str RGB模式下的模型预训练权重文件的路径
flow_mdir: str FLOW模式下的模型预训练权重文件的路径
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