Update README.md

README.md

@@ -14,8 +14,9 @@ This repo is the official implementation of [PoseConv3D](https://arxiv.org/abs/2
 
 ## News
 
-- We release the skeleton annotations (generated by HRNet), config files, and pre-trained checkpoints for Kinetics-400. Note that Kinetics-400 is a large-scale dataset (even for skeleton) and you should have `memcached` and `pymemcache` installed for efficient training and testing on Kinetics-400. <**2022-05-01**>
-- We provide an example for processing a custom video dataset (we use diving48), generating 2D skeleton annotations, and using PoseC3D for skeleton-based action recognition. The tutorial for skeleton extraction part is available in [diving48_example](/examples/extract_diving48_skeleton/diving48_example.ipynb).  <**2022-04-15**>
+- Support skeleton action recognition demo with GCN algorithms  (**2022-05-03**).
+- Release the skeleton annotations (generated by HRNet), config files, and pre-trained checkpoints for Kinetics-400. Note that Kinetics-400 is a large-scale dataset (even for skeleton) and you should have `memcached` and `pymemcache` installed for efficient training and testing on Kinetics-400 (**2022-05-01**).
+- Provide an example for processing a custom video dataset (we use diving48), generating 2D skeleton annotations, and using PoseC3D for skeleton-based action recognition. The tutorial for skeleton extraction part is available in [diving48_example](/examples/extract_diving48_skeleton/diving48_example.ipynb)  (**2022-04-15**).
 
 ## Supported Algorithms
 
@@ -54,7 +55,7 @@ python demo/demo_skeleton.py demo/ntu_sample.avi demo/demo.mp4
 python demo/demo_skeleton.py demo/ntu_sample.avi demo/demo.mp4 --config configs/stgcn++/stgcn++_ntu120_xsub_hrnet/j.py --ckpt http://download.openmmlab.com/mmaction/pyskl/ckpt/stgcnpp/stgcnpp_ntu120_xsub_hrnet/j.pth
 ```
 
-Note that for running demo on an arbitrary input video, you need a tracker to formulate pose estimation results for each frame into multiple skeleton sequences. Currently we are using a [naive tracker]() based on inter-frame pose similarities. You can also try to write your own tracker.
+Note that for running demo on an arbitrary input video, you need a tracker to formulate pose estimation results for each frame into multiple skeleton sequences. Currently we are using a [naive tracker](https://github.com/kennymckormick/pyskl/blob/4ddb7ac384e231694fd2b4b7774144e5762862ab/demo/demo_skeleton.py#L192) based on inter-frame pose similarities. You can also try to write your own tracker.
 
 ## Training & Testing