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 531–540 of 1322 papers

Title	Date	Tasks	Status
Continuous Human Activity Recognition using a MIMO Radar for Transitional Motion Analysis	Apr 12, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey	Apr 7, 2023	Activity RecognitionDomain Adaptation	—Unverified
VicTR: Video-conditioned Text Representations for Activity Recognition	Apr 5, 2023	Action ClassificationActivity Recognition	—Unverified
Multi-Channel Time-Series Person and Soft-Biometric Identification	Apr 4, 2023	Activity RecognitionAttribute	—Unverified
WSense: A Robust Feature Learning Module for Lightweight Human Activity Recognition	Mar 31, 2023	Activity RecognitionHuman Activity Recognition	CodeCode Available
Channel Phase Processing in Wireless Networks for Human Activity Recognition	Mar 29, 2023	Activity RecognitionDenoising	—Unverified
Sensing with OFDM Waveform at mmWave Band based on Micro-Doppler Analysis	Mar 29, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentation	Mar 29, 2023	Activity RecognitionClustering	CodeCode Available
Provable Robustness for Streaming Models with a Sliding Window	Mar 28, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling	Mar 27, 2023	Action LocalizationAction 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