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 1201–1210 of 1322 papers

Title	Date	Tasks	Status
Ratio Utility and Cost Analysis for Privacy Preserving Subspace Projection	Feb 26, 2017	Activity RecognitionHuman Activity Recognition	—Unverified
Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies	Feb 24, 2017	Activity RecognitionMachine Translation	—Unverified
Boosted Multiple Kernel Learning for First-Person Activity Recognition	Feb 22, 2017	Activity Recognition	—Unverified
An Analysis of Parallelized Motion Masking Using Dual-Mode Single Gaussian Models	Feb 16, 2017	Activity RecognitionMotion Detection	CodeCode Available
Concurrent Activity Recognition with Multimodal CNN-LSTM Structure	Feb 6, 2017	Activity RecognitionConcurrent Activity Recognition	—Unverified
Probabilistic Sensor Fusion for Ambient Assisted Living	Feb 4, 2017	Activity RecognitionSensor Fusion	—Unverified
Self-Adaptation of Activity Recognition Systems to New Sensors	Jan 30, 2017	Activity RecognitionClustering	—Unverified
An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition	Jan 4, 2017	Activity RecognitionDiversity	—Unverified
A Deep Learning Approach To Multiple Kernel Fusion	Dec 28, 2016	Activity RecognitionDeep Learning	—Unverified
Human Action Attribute Learning From Video Data Using Low-Rank Representations	Dec 23, 2016	Action RecognitionActivity Recognition	—Unverified

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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