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

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Showing 431–440 of 1322 papers

Title	Date	Tasks	Status
SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition	Apr 27, 2023	Activity RecognitionGroup Activity Recognition	—Unverified
Human Activity Recognition Using Self-Supervised Representations of Wearable Data	Apr 26, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Evaluation of Regularization-based Continual Learning Approaches: Application to HAR	Apr 26, 2023	Activity RecognitionContinual Learning	—Unverified
RHM: Robot House Multi-view Human Activity Recognition Dataset	Apr 24, 2023	Action RecognitionActivity Recognition	CodeCode Available
A Survey on Multi-Resident Activity Recognition in Smart Environments	Apr 24, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Fruit Picker Activity Recognition with Wearable Sensors and Machine Learning	Apr 20, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Big-Little Adaptive Neural Networks on Low-Power Near-Subthreshold Processors	Apr 19, 2023	Activity RecognitionHuman Activity Recognition	CodeCode Available
Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection	Apr 19, 2023	Activity RecognitionAnomaly Detection	—Unverified
SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning	Apr 19, 2023	Active LearningActivity Recognition	—Unverified
Human activity recognition using deep learning approaches and single frame cnn and convolutional lstm	Apr 18, 2023	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