diff --git a/deploy/pphuman/README_en.md b/deploy/pphuman/README_en.md index 2cef53d511eb00835ce0c811c7ce3ba2d4f9121f..d45481b4d5f8da088b251997bbe8266146b72c9f 100644 --- a/deploy/pphuman/README_en.md +++ b/deploy/pphuman/README_en.md @@ -3,7 +3,7 @@ English | [简体中文](README.md) # PP-Human— a Real-Time Pedestrian Analysis Tool PP-Human serves as the first open-source tool of real-time pedestrian anaylsis relying on the PaddlePaddle deep learning framework. Versatile and efficient in deployment, it has been used in various senarios. PP-Human -offers many input options, including image/single-camera video/multi-camera video, and covers multi-object tracking, attribute recognition, and behavior analysis. PP-Human can be applied to intelligent traffic, the intelligent community, industiral patrol, and so on. It supports server-side deployment and TensorRT acceleration，and even can achieve real-time analysis on the T4 server. +offers many input options, including image/single-camera video/multi-camera video, and covers multi-object tracking, attribute recognition, and action recognition. PP-Human can be applied to intelligent traffic, the intelligent community, industiral patrol, and so on. It supports server-side deployment and TensorRT acceleration，and even can achieve real-time analysis on the T4 server. ## I. Environment Preparation @@ -40,9 +40,9 @@ To make users have access to models of different scenarios, PP-Human provides pr | :---------: |:---------: |:--------------- | :-------: | :------: | | Object Detection | Image/Video Input | - | - | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | | Attribute Recognition | Image/Video Input Attribute Recognition | - | - | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.tar) | -| Keypoint Detection | Video Input Behavior Recognition | - | - | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip) +| Keypoint Detection | Video Input Action Recognition | - | - | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip) | Behavior Recognition | Video Input Bheavior Recognition | - | - | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) | -| ReID | Video Input Cross Cross-camera Tracking | - | - | [Link]() | +| ReID | Multi-Target Multi-Camera Tracking | - | - | [Link]() | Then, unzip the downloaded model to the folder `./output_inference`. @@ -61,7 +61,7 @@ Their correspondence is as follows: |-------|-------|----------|-----| | Image | Attribute Recognition | Object Detection Attribute Recognition | DET ATTR | | Single-Camera Video | Attribute Recognition | Multi-Object Tracking Attribute Recognition | MOT ATTR | -| Single-Camera Video | Behavior Recognition | Multi-Object Tracking Keypoint Detection Behavior Recognition | MOT KPT ACTION | +| Single-Camera Video | Behavior Recognition | Multi-Object Tracking Keypoint Detection Action Recognition | MOT KPT ACTION | For example, for the attribute recognition with the video input, its task types contain multi-object tracking and attribute recognition, and the config is: @@ -91,7 +91,7 @@ python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml - # Specify the config file path and test videos，and finish the attribute recognition python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml --video_file=test_video.mp4 --device=gpu --enable_attr=True -# Specify the config file path and test videos，and finish the behavior recognition +# Specify the config file path and test videos，and finish the Action Recognition python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml --video_file=test_video.mp4 --device=gpu --enable_action=True # Specify the config file path, the model path and test videos，and finish the multi-object tracking @@ -110,7 +110,7 @@ python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml - | --video_file | Option | Videos to-be-predicted | | --camera_id | Option | ID of the inference camera is -1 by default (means inference without cameras，and it can be set to 0 - (number of cameras-1)), and during the inference, click `q` on the visual interface to exit and output the inference result to output/output.mp4| | --enable_attr| Option | Enable attribute recognition or not | -| --enable_action| Option | Enable behavior recognition or not | +| --enable_action| Option | Enable action recognition or not | | --device | Option | During the operation，available devices are `CPU/GPU/XPU`，and the default is `CPU`| | --output_dir | Option| The default root directory which stores the visualization result is output/| | --run_mode | Option | When using GPU，the default one is paddle, and all these are available（paddle/trt_fp32/trt_fp16/trt_int8）.| @@ -144,13 +144,13 @@ The overall solution of PP-Human is as follows: - Match the features of multi-camera tracks to get the cross-camera tracking result - For details, please refer to [Cross-Camera Tracking](docs/mtmct_en.md) -### 4. Attribute Recognition +### 4. Multi-Target Multi-Camera Tracking - Use PP-YOLOE + Bytetrack to track humans - Use StrongBaseline（a multi-class model）to conduct attribute recognition, and the main attributes include age, gender, hats, eyes, clothing, and backpacks. - For details, please refer to [Attribute Recognition](docs/attribute_en.md) -### 5. Behavior Recognition +### 5. Action Recognition - Use PP-YOLOE + Bytetrack to track humans - Use HRNet for keypoint detection and get the information of the 17 key points in the human body - According to the changes of the key points of the same person within 50 frames, judge whether the action made by the person within 50 frames is a fall with the help of ST-GCN -- For details, please refer to [Behavior Recognition](docs/action_en.md) +- For details, please refer to [Action Recognition](docs/action_en.md)