该工程基于PaddleDetection实现人员摔倒识别。先通过特定命令预测视频关键点，得到json结果文件和可视化视频；再用source.py中代码判断摔倒，输出摔倒帧对应时间，还会在视频检测框左上角标注结果并保存为output.mp4，示例中检测到78帧摔倒。
☞☞☞AI 智能聊天, 问答助手, AI 智能搜索, 免费无限量使用 DeepSeek R1 模型☜☜☜
基于PaddleDetection的人员摔倒识别
本工程是基于PaddleDetection人员关键点预测的摔倒识别案例
关于PaddleDetection的使用详情请参考github链接 PaddleDetection
1 获取视频关键点预测结果
在PaddleDetection下使用如下命令预测视频结果(相关模型可以从github中model_zoo获取):
python deploy/python/det_keypoint_unite_infer.py --det_model_dir=output_inference/ppyolo_r50vd_dcn_2x_coco/ --keypoint_model_dir=output_inference/hrnet_w32_256x192/ --video_file=./pose_demo/test.mp4 --device=gpu --save_res=True
预测完成后会在当前目录下得到预测结果json文件：det_keypoint_unite_video_results.json, 即此处使用的kpts_results.json文件.
在output目录下得到预测结果可视化文件：test.mp4, 即此处的video.mp4文件.
2 根据关键点结果判断是否摔倒
执行最下面命令行的命令，对关键点预测结果kpts_results.json做摔倒逻辑的判断，摔倒帧对应时间会打印显示。同时会在视频上对应时间的检测框左上角打印fall_down结果，结果视频保存为output.mp4。
- 逻辑判断代码在source.py文件中
In [1]
import osimport sysimport cv2import numpy as npimport jsonimport collectionsfrom source import check_fall_down, videovis
In [3]
#1）脚本第一个参数为关键点预测结果json文件jsonf = "kpts_results.json"with open(jsonf, "r") as rf:
kpts_data = json.load(rf)print("all data length: {}".format(len(kpts_data)))#2）如果需要视频打印摔倒文字，关键点可视化结果文件放在同路径videof = "video.mp4"#3）读取关键点结果后放入判断文件fallframes = check_fall_down(kpts_data)#4）根据检测的摔倒帧在视频显示videovis(videof, kpts_data, fallframes)
all data length: 468 fall_down frames: 78 time: 5.5s, fall down detected time: 6.0s, fall down detected time: 6.5s, fall down detected time: 7.0s, fall down detected time: 7.5s, fall down detected time: 8.0s, fall down detected time: 8.5s, fall down detected time: 9.0s, fall down detected time: 9.5s, fall down detected time: 10.0s, fall down detected time: 10.5s, fall down detected time: 11.0s, fall down detected time: 11.5s, fall down detected time: 12.0s, fall down detected time: 12.5s, fall down detected time: 18.0s, fall down detected time: 18.5s, fall down detected time: 19.5s, fall down detected time: 20.5s, fall down detected time: 21.0s, fall down detected time: 21.5s, fall down detected time: 23.5s, fall down detected time: 25.5s, fall down detected time: 26.0s, fall down detected time: 29.5s, fall down detected time: 30.0s, fall down detected time: 32.0s, fall down detected time: 32.5s, fall down detected time: 33.0s, fall down detected time: 35.0s, fall down detected time: 35.5s, fall down detected time: 36.0s, fall down detected time: 38.0s, fall down detected time: 38.5s, fall down detected print fall down result in video: video.mp4 fps: 12, frame_count: 468
