- Object Detection
- Object tracking
- Multiple Object Tracking (MOT)
- Human Action Detection
- Human Activity Recognition (HAR) Resarch
Action Recognition Wiki Human Action Recognition Wiki Multi Object Tracking Wiki
Human activity recognition, or HAR, is a challenging time series classification task.
It involves predicting the movement of a person based on sensor data and traditionally involves deep domain expertise and methods from signal processing to correctly engineer features from the raw data in order to fit a machine learning model.
Recently, deep learning methods such as convolutional neural networks and recurrent neural networks have shown capable and even achieve state-of-the-art results by automatically learning features from the raw sensor data.
OS X & Linux:
To-DoWindows:
To-Do- Activity recognition is the problem of predicting the movement of a person, often indoors, based on sensor data, such as an accelerometer in a smartphone.
- Streams of sensor data are often split into subs-sequences called windows, and each window is associated with a broader activity, called a sliding window approach.
- Convolutional neural networks and long short-term memory networks, and perhaps both together, are best suited to learning features from raw sensor data and predicting the associated movement.
For more examples and usage, please refer to the Wiki.
Describe how to install all development dependencies and how to run an automated test-suite of some kind. Potentially do this for multiple platforms.
run install.batMajor,Minor,Bug
- 0.0.0
- Work in progress
Ümit Mert ÇAĞLAR – @Blog – mert.caglar@metu.edu.tr
Distributed under the MIT license. See LICENSE for more information.
- Fork it (https://github.com/yourname/yourproject/fork)
- Create your feature branch (
git checkout -b feature/fooBar) - Commit your changes (
git commit -am 'Add some fooBar') - Push to the branch (
git push origin feature/fooBar) - Create a new Pull Request