Activity Recognition

Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.

Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1181–1190 of 1322 papers

Title	Date	Tasks	Status
Application of Transfer Learning Approaches in Multimodal Wearable Human Activity Recognition	Jul 8, 2017	Activity RecognitionEnsemble Learning	—Unverified
Structure Optimization for Deep Multimodal Fusion Networks using Graph-Induced Kernels	Jul 3, 2017	Activity RecognitionBayesian Optimization	—Unverified
Recurrent Modeling of Interaction Context for Collective Activity Recognition	Jul 1, 2017	Activity RecognitionDescriptive	—Unverified
Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network	Jul 1, 2017	Activity RecognitionFace Alignment	—Unverified
Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging	Jul 1, 2017	Activity Recognitionregression	—Unverified
Re-Sign: Re-Aligned End-To-End Sequence Modelling With Deep Recurrent CNN-HMMs	Jul 1, 2017	Activity RecognitionGesture Recognition	—Unverified
Hierarchical Attentive Recurrent Tracking	Jun 28, 2017	Activity RecognitionObject	CodeCode Available
Cross-Country Skiing Gears Classification using Deep Learning	Jun 27, 2017	Activity RecognitionClassification	—Unverified
MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU	Jun 3, 2017	Activity RecognitionGPU	CodeCode Available
Non-Uniform Subset Selection for Active Learning in Structured Data	Jun 1, 2017	Active LearningActivity Recognition	CodeCode Available

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All datasets RWF-2000 Stanford40 First-Person Hand Action Benchmark Self-stimulatory Behavior Dataset

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Structured Keypoint Pooling	Accuracy	93.4	—	Unverified
2	Semi-Supervised Hard Attention (SSHA); pretrained on Deepmind Kinetics dataset	Accuracy	90.4	—	Unverified
3	Human Skeletons + Change Detection	Accuracy	90.25	—	Unverified
4	Separable Convolutional LSTM	Accuracy	89.75	—	Unverified
5	SPIL Convolution	Accuracy	89.3	—	Unverified
6	Flow Gated Network	Accuracy	87.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FocusCLIP	Top-3 Accuracy (%)	10.47	—	Unverified
2	CLIP	Top-3 Accuracy (%)	6.49	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Boutaleb et al.	1:1 Accuracy	97.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	all-landmark-model	Activity Recognition	0.76	—	Unverified