Safe Goodbye
TEAM: Arrival(Jungyeon Lee/Chaeeun Kim/Yookyung Jeong)
If you click the image below, you can see the presentation video on Youtube.
Dataset
자율주행 - 버스 승객 승하차 영상 : Camera C (General C)
- Removed List
    
>>> image problem ...\ele\[ele]attend_270C ...\etc\[etc]attend_154C ...\hospital\[hospital]attend_392C ...\hospital\[hospital]leave_251C >>> label problem ...\apt\[apt]attend_1275C ...\apt\[apt]attend_941C - Target(demonstration result) from Validation set
 
reselecting
type 1
[hospital]attend_067C
type 2
[mid_high]leave_354C
  
| Train | Valid | |
|---|---|---|
| per Scene | ![]()  | 
      ![]()  | 
    
| per Frame | ![]()  | 
      ![]()  | 
    
  
Modeling
- 하차 전 약 35프레임에서의 각 사람의 skeleton data를 time series data로 만들어서 하차할 것인지 하차하지 않을 것인지 Intention Prediction
 
  
| Train | Valid | |
|---|---|---|
| Loss | ||
| Accuracy | 
Codes
data_analysis.py: 1make_dataset.py: 2dataset.py: 3simple_dataset_keras.py: 4 simple lstm model로 trainingtrain.py: 5generate_video.py: 6tf2_multipose.py: todo
Reference
- Scale invariant angle label
 - seaborn.histplot
 - matplotlib barplot
 - AngleAnnotation class
 - dealing with csv file
 - Keras data generator
 - Guide to the Functional API
 - DataGenerator
 - Tensorflow Keras - 4 (자연어처리,감정분석)
 - Tensorborad
 - Checkpoint
 - ModelCheckpoint
 
Baseline
- Unified Framework for Pedestrian Detection & Intention Classification
 - FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Networks
 
OD+Tracking
- Yolov5 + Deep Sort with PyTorch
    
python track.py --source [village]day_001B.mp4 --yolo_weights yolov5/weights/crowdhuman_yolov5m.pt --classes 0 --save-txt --save-vidframe_idx, id, bbox_left, bbox_top, bbox_w, bbox_h, -1, -1, -1, -1)
 - YOLOv4-Cloud-Tutorial
 
Pose Estimation
- Deep High-Resolution Representation Learning for Human Pose Estimation (CVPR 2019)
 - Multi Person PoseEstimation By PyTorch
    
python Demo_video.py -backbone {CMU or Mobilenet} -video {video path} -scale {scale to image} -show {}
 



