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 471–480 of 1322 papers

Title	Date	Tasks	Status
BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition	Aug 7, 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
Evaluation of Encoding Schemes on Ubiquitous Sensor Signal for Spiking Neural Network	Jul 12, 2024	Activity Recognition	—Unverified
Evaluation of Regularization-based Continual Learning Approaches: Application to HAR	Apr 26, 2023	Activity RecognitionContinual Learning	—Unverified
Event and Activity Recognition in Video Surveillance for Cyber-Physical Systems	Nov 3, 2021	Action RecognitionActivity Recognition	—Unverified
Event-LSTM: An Unsupervised and Asynchronous Learning-based Representation for Event-based Data	May 10, 2021	Activity RecognitionGesture Recognition	—Unverified
EventSleep: Sleep Activity Recognition with Event Cameras	Apr 2, 2024	Activity Recognition	—Unverified
Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams	Nov 26, 2024	Activity RecognitionEEG	—Unverified
Expanding Frozen Vision-Language Models without Retraining: Towards Improved Robot Perception	Aug 31, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Exploring the Capabilities of LLMs for IMU-based Fine-grained Human Activity Understanding	Apr 2, 2025	Activity RecognitionFew-Shot 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