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 911–920 of 1322 papers

Title	Date	Tasks	Status
Approaches and Applications of Early Classification of Time Series: A Review	May 6, 2020	Activity RecognitionClassification	—Unverified
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition	May 5, 2020	Activity RecognitionDecision Making	—Unverified
Pedestrian Path, Pose and Intention Prediction through Gaussian Process Dynamical Models and Pedestrian Activity Recognition	Apr 30, 2020	Activity Recognition	—Unverified
Skeleton Focused Human Activity Recognition in RGB Video	Apr 29, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
EmbraceNet for Activity: A Deep Multimodal Fusion Architecture for Activity Recognition	Apr 29, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
Effective Human Activity Recognition Based on Small Datasets	Apr 29, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
HAPRec: Hybrid Activity and Plan Recognizer	Apr 28, 2020	Action RecognitionActivity Recognition	—Unverified
Using GAN to Enhance the Accuracy of Indoor Human Activity Recognition	Apr 23, 2020	Activity RecognitionGenerative Adversarial Network	—Unverified
Group Activity Detection from Trajectory and Video Data in Soccer	Apr 21, 2020	Action DetectionActivity Detection	—Unverified
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey	Apr 19, 2020	Activity RecognitionBIG-bench Machine Learning	—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