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 561–570 of 1322 papers

Title	Date	Tasks	Status
Gated Recurrent Neural Networks with Weighted Time-Delay Feedback	Dec 1, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Generalizable Indoor Human Activity Recognition Method Based on Micro-Doppler Corner Point Cloud and Dynamic Graph Learning	Oct 10, 2024	Activity RecognitionDecision Making	—Unverified
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning	May 25, 2023	Activity RecognitionContrastive Learning	—Unverified
Generalizable Sensor-Based Activity Recognition via Categorical Concept Invariant Learning	Dec 18, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Generalization Ability Analysis of Through-the-Wall Radar Human Activity Recognition	Oct 10, 2024	Activity RecognitionDimensionality Reduction	—Unverified
Generalized Rank Pooling for Activity Recognition	Apr 7, 2017	Action RecognitionActivity Recognition	—Unverified
Compact CNN for Indexing Egocentric Videos	Apr 28, 2015	Activity RecognitionOptical Flow Estimation	—Unverified
Out-of-Distribution Representation Learning for Time Series Classification	Sep 15, 2022	Activity RecognitionClassification	—Unverified
Appearance Based Robot and Human Activity Recognition System	Feb 4, 2016	Activity RecognitionHuman Activity Recognition	—Unverified
Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint	Dec 31, 2013	Activity Recognitionfeature selection	—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