Update README.md

README.md

@@ -1,3 +1,32 @@
 ---
+tags:
+- pixel-tracking
+- computer-vision
 license: mit
+library: pytorch
+inference: false
 ---
+
+# PIPS: Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories
+
+* Model Authors: Adam W Harley and Zhaoyuan Fang and Katerina Fragkiadaki
+* Paper: Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories (ECCV 2022 - https://arxiv.org/abs/2204.04153
+* Code Repo: https://github.com/aharley/pips
+* Project Homepage: https://particle-video-revisited.github.io
+
+From the paper abstract:
+
+> [...] we revisit Sand and Teller's "particle video" approach, and study pixel tracking as a long-range motion estimation problem, where every pixel is described with a trajectory that locates it in multiple future frames. We re-build this classic approach using components that drive the current state-of-the-art in flow and object tracking, such as dense cost maps, iterative optimization, and learned appearance updates. We train our models using long-range amodal point trajectories mined from existing optical flow data that we synthetically augment with multi-frame occlusions.
+
+![](https://camo.githubusercontent.com/6313516710b2b7dcb03deebcd423f39f77aa03f0ad378e30a876e807e5391cee/68747470733a2f2f7061727469636c652d766964656f2d7265766973697465642e6769746875622e696f2f696d616765732f666967312e6a7067)
+
+# Citation
+
+```
+@inproceedings{harley2022particle,
+  title={Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories},
+  author={Adam W Harley and Zhaoyuan Fang and Katerina Fragkiadaki},
+  booktitle={ECCV},
+  year={2022}
+}
+```