Displaying 1 to 6 from 6 results

tf-pose-estimation - Deep Pose Estimation implemented using Tensorflow with Custom Architectures for fast inference

  •    PureBasic

'Openpose' for human pose estimation have been implemented using Tensorflow. It also provides several variants that have made some changes to the network structure for real-time processing on the CPU or low-power embedded devices. 2018.5.21 Post-processing part is implemented in c++. It is required compiling the part. See: https://github.com/ildoonet/tf-pose-estimation/tree/master/src/pafprocess 2018.2.7 Arguments in run.py script changed. Support dynamic input size.

openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, and hands estimation

  •    C++

OpenPose represents the first real-time multi-person system to jointly detect human body, hand, and facial keypoints (in total 135 keypoints) on single images. For further details, check all released features and release notes.

ros-openpose - CMU's OpenPose for ROS

  •    C++

I re-implemented cmu's openpose in tensorflow with some modifications. Especially, using Mobilenet's 'Depthwise Separable Convolution', I improved it to run in realtime even in an low-computation embedded deivce or only-cpu environment.

MAX-Human-Pose-Estimator - Detect humans in an image and estimate the pose for each person

  •    Jupyter

The Human Pose Estimator model detects humans and their poses in a given image. The model first detects the humans in the input image and then identifies the body parts, including nose, neck, eyes, shoulders, elbows, wrists, hips, knees, and ankles. Next, each pair of associated body parts is connected by a "pose line"; for example, as the following image shows, a line may connect the left eye to the nose, while another may connect the nose to the neck. Each pose line is represented by a list [x1, y1, x2, y2], where the first pair of coordinates (x1, y1) is the start point of the line for one body part, while the second pair of coordinates (x2, y2) is the end point of the line for the other associated body part. The pose lines are assembled into full body poses for each of the humans detected in the image.




openpose_caffe_train - https://github.com/CMU-Perceptual-Computing-Lab/openpose

  •    C++

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors. and step-by-step examples.