智慧教室

课堂专注度及考试作弊系统、课堂动态点名，情绪识别、表情识别和人脸识别结合

课堂专注度分析

课堂专注度+表情识别

作弊检测

关键点计算方法

转头(probe)+低头(peep)+传递物品(passing)

侧面的传递物品识别

逻辑回归关键点

下载权重

1. Halpe dataset (136 keypoints)

Model	Backbone	Detector	Input Size	AP	Speed	Download	Config	Training Log
Fast Pose	ResNet50	YOLOv3	256x192	69.0	3.54 iter/s	Google Baidu	cfg	log

放到detection_system/checkpoints

2. Human-ReID based tracking (Recommended)

Currently the best performance tracking model. Paper coming soon.

Getting started

Download human reid model and place it into AlphaPose/trackers/weights/.

Then simply run alphapose with additional flag --pose_track

You can try different person reid model by modifing cfg.arch and cfg.loadmodel in ./trackers/tracker_cfg.py.

If you want to train your own reid model, please refer to this project

3. Yolo Detector

Download the object detection model manually: yolov3-spp.weights(Google Drive | Baidu pan). Place it into detector/yolo/data.

4. face boxes 预训练权重

google drive

放到face_recog/weights文件夹下

5. 其他

百度云提取码：rwtl

人脸识别：dlib_face_recognition_resnet_model_v1.dat

detection_system/face_recog/weights

人脸对齐：shape_predictor_68_face_landmarks.dat

detection_system/face_recog/weights

作弊动作分类器：cheating_detector_rfc_kp.pkl

detection_system/weights

使用

运行setup.py安装必要内容

python setup.py build develop

windows上安装scipy1.1.0可能会遇到的问题

运行demo_inference.py

将detection_system设置为source root

使用摄像头运行程序

python demo_inference.py --vis --webcam 0

MicroEngine/smart_classroom