StepSTEP: Spatio-Temporal Progressive Learning for Video Action Detection. CVPR'19 (Oral)
Video CaffeVideo-friendly caffe -- comes with the most recent version of Caffe (as of Jan 2019), a video reader, 3D(ND) pooling layer, and an example training script for C3D network and UCF-101 data
Motion SenseMotionSense Dataset for Human Activity and Attribute Recognition ( time-series data generated by smartphone's sensors: accelerometer and gyroscope)
TimeceptionTimeception for Complex Action Recognition, CVPR 2019 (Oral Presentation)
HakeHAKE: Human Activity Knowledge Engine (CVPR'18/19/20, NeurIPS'20)
Intro To Cv Ud810Problem Set solutions for the "Introduction to Computer Vision (ud810)" MOOC from Udacity
M PactA one stop shop for all of your activity recognition needs.
Hake ActionAs a part of the HAKE project, includes the reproduced SOTA models and the corresponding HAKE-enhanced versions (CVPR2020).
WdkThe Wearables Development Toolkit - a development environment for activity recognition applications with sensor signals
SenseEnhance your application with the ability to see and interact with humans using any RGB camera.
Lstm Human Activity RecognitionHuman Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier
Robust-Deep-Learning-PipelineDeep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data. Human Activity Recognition Challenge. Springer SIST (2020)
hamnetPyTorch implementation of AAAI 2021 paper: A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization
danaDANA: Dimension-Adaptive Neural Architecture (UbiComp'21)( ACM IMWUT)
Squeeze-and-Recursion-Temporal-GatesCode for : [Pattern Recognit. Lett. 2021] "Learn to cycle: Time-consistent feature discovery for action recognition" and [IJCNN 2021] "Multi-Temporal Convolutions for Human Action Recognition in Videos".
Awesome-Human-Activity-RecognitionAn up-to-date & curated list of Awesome IMU-based Human Activity Recognition(Ubiquitous Computing) papers, methods & resources. Please note that most of the collections of researches are mainly based on IMU data.
glimpse cloudsPytorch implementation of the paper "Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points", F. Baradel, C. Wolf, J. Mille , G.W. Taylor, CVPR 2018
stipcvRealtime implemnetation of spatial-temporal local features
R2Plus1D-C3DA PyTorch implementation of R2Plus1D and C3D based on CVPR 2017 paper "A Closer Look at Spatiotemporal Convolutions for Action Recognition" and CVPR 2014 paper "Learning Spatiotemporal Features with 3D Convolutional Networks"