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 1021–1030 of 1322 papers

Title	Date	Tasks	Status
PI-Net: A Deep Learning Approach to Extract Topological Persistence Images	Jun 5, 2019	Activity RecognitionDeep Learning	CodeCode Available
ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity Recognition	May 31, 2019	Active LearningActivity Recognition	CodeCode Available
Personalizing human activity recognition models using incremental learning	May 29, 2019	Activity RecognitionGeneral Classification	—Unverified
From User-independent to Personal Human Activity Recognition Models Exploiting the Sensors of a Smartphone	May 29, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
Importance of user inputs while using incremental learning to personalize human activity recognition models	May 28, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
Multi-agent Attentional Activity Recognition	May 22, 2019	Activity Recognition	—Unverified
Activity Recognition and Prediction in Real Homes	May 20, 2019	Activity RecognitionPrediction	—Unverified
Disparity-Augmented Trajectories for Human Activity Recognition	May 14, 2019	3D ReconstructionActivity Recognition	—Unverified
Federated Multi-task Hierarchical Attention Model for Sensor Analytics	May 13, 2019	Activity RecognitionGeneral Classification	—Unverified
On Flow Profile Image for Video Representation	May 12, 2019	Activity RecognitionOptical Flow Estimation	—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