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 521–530 of 1322 papers

Title	Date	Tasks	Status
Unsupervised Deep Learning-based clustering for Human Activity Recognition	Nov 10, 2022	Activity RecognitionClustering	CodeCode Available
SWTF: Sparse Weighted Temporal Fusion for Drone-Based Activity Recognition	Nov 10, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data	Nov 8, 2022	Activity Recognition	—Unverified
Multi-Stage Based Feature Fusion of Multi-Modal Data for Human Activity Recognition	Nov 8, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
XAI-BayesHAR: A novel Framework for Human Activity Recognition with Integrated Uncertainty and Shapely Values	Nov 7, 2022	Activity RecognitionExplainable Artificial Intelligence (XAI)	—Unverified
Predicting User-specific Future Activities using LSTM-based Multi-label Classification	Nov 6, 2022	Activity PredictionActivity Recognition	—Unverified
Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity?	Nov 5, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Discriminating sensor activation in activity recognition within multi-occupancy environments based on nearby interaction	Nov 3, 2022	Activity Recognition	—Unverified
Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Data	Nov 2, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones	Oct 30, 2022	Activity RecognitionFederated 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