Merge pull request #593 from microsoft/staging

Sync master <-> staging

Author: PatrickBue

README.md

@@ -1,8 +1,8 @@
 <img src="scenarios/media/logo_cvbp.png" align="right" alt="" width="300"/>
 
 ```diff
-+ Update June 24: Added action recognition as new core scenario.
-+                 Object tracking coming soon (in 2-4 weeks).
++ Update July: Added support for action recognition and tracking
++              in the new release v1.2.
 ```
 
 # Computer Vision
@@ -55,6 +55,7 @@ The following is a summary of commonly used Computer Vision scenarios that are c
 | [Keypoints](scenarios/keypoints) | Base | Keypoint detection can be used to detect specific points on an object. A pre-trained model is provided to detect body joints for human pose estimation. |
 | [Segmentation](scenarios/segmentation) | Base | Image Segmentation assigns a category to each pixel in an image. |
 | [Action recognition](scenarios/action_recognition) | Base | Action recognition to identify in video/webcam footage what actions are performed (e.g. "running", "opening a bottle") and at what respective start/end times. We also implemented the i3d implementation of action recognition that can be found under (contrib)[contrib]. |
+| [Tracking](scenarios/tracking) | Base | Tracking allows to detect and track multiple objects in a video sequence over time. |
 | [Crowd counting](contrib/crowd_counting) | Contrib | Counting the number of people in low-crowd-density (e.g. less than 10 people) and high-crowd-density (e.g. thousands of people) scenarios.|
 
 We separate the supported CV scenarios into two locations: (i) **base**: code and notebooks within the "utils_cv" and "scenarios" folders which follow strict coding guidelines, are well tested and maintained; (ii) **contrib**: code and other assets within the "contrib" folder, mainly covering less common CV scenarios using bleeding edge state-of-the-art approaches. Code in "contrib" is not regularly tested or maintained.

scenarios/README.md

@@ -8,6 +8,7 @@
 | [Keypoints](keypoints) | Keypoint Detection can be used to detect specific points on an object. A pre-trained model is provided to detect body joints for human pose estimation. |
 | [Segmentation](segmentation) | Image Segmentation assigns a category to each pixel in an image. |
 | [Action Recognition](action_recognition) | Action Recognition (also known as activity recognition) consists of classifying various actions from a sequence of frames, such as "reading" or "drinking". |
+| [Tracking](tracking) | Tracking allows to detect and track multiple objects in a video sequence over time. |
 
 
 # Scenarios