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 511–520 of 1322 papers

Title	Date	Tasks	Status
Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity Recognition	May 19, 2023	Activity Recognition	—Unverified
rWISDM: Repaired WISDM, a Public Dataset for Human Activity Recognition	May 17, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors	May 15, 2023	Activity RecognitionHuman Activity Recognition	CodeCode Available
Is end-to-end learning enough for fitness activity recognition?	May 14, 2023	Action RecognitionActivity Recognition	—Unverified
Group Activity Recognition via Dynamic Composition and Interaction	May 9, 2023	Activity RecognitionGroup Activity Recognition	—Unverified
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition	May 5, 2023	Activity RecognitionFeature Engineering	—Unverified
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

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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