d1vyansh Goto Github PK

bodyjointdetection

Human Pose Estimation is a very challenging task with intensive research interest due to its various applications, such as animation, gaming, human-computer interaction, augmentedreality,humanbehavioranalysisandsportsperformance analysis. Estimating the pose of a human in an image or a video has recently received significant amount of attention from the scientific community

competitive_programming

Solved solutions for various coding challenges in which I participated

eye_blink_detection-from-scratch

Detecting open and close eyes by coding the mathematical computation using eye aspect ratio formula

facialrecognition

flynet

Official PyTorch implementation of the paper "A Hybrid Compact Neural Architecture for Visual Place Recognition" by M. Chancán et al. (RA-L & ICRA 2020) https://doi.org/10.1109/LRA.2020.2967324

handypose

We propose HandyPose, a single-stage network for hand pose estimation that is end to end trainable and produces state-of-the-art results. HandyPose incorporates contextual segmentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. To deal with the challenges of hand pose context and resolution, our architecture generates improved multi-scale and multi-level representations by combining features from multiple levels of the backbone network via our advanced Multi-Level Waterfall module.

master

openpose

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

restricted-zone-detection

Restricted Zone Detection implemented with YOLOv4, DeepSort, OpenCV and TensorFlow.

unipose

We propose UniPose, a unified framework for human pose estimation, based on our “Waterfall” Atrous Spatial Pooling architecture, that achieves state-of-art-results on several pose estimation metrics. Current pose estimation methods utilizing standard CNN architectures heavily rely on statistical postprocessing or predefined anchor poses for joint localization. UniPose incorporates contextual seg- mentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. The Waterfall module in UniPose leverages the efficiency of progressive filter- ing in the cascade architecture, while maintaining multi- scale fields-of-view comparable to spatial pyramid config- urations. Additionally, our method is extended to UniPose- LSTM for multi-frame processing and achieves state-of-the- art results for temporal pose estimation in Video. Our re- sults on multiple datasets demonstrate that UniPose, with a ResNet backbone and Waterfall module, is a robust and efficient architecture for pose estimation obtaining state-of- the-art results in single person pose detection for both sin- gle images and videos.