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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 691–700 of 1322 papers

Title	Date	Tasks	Status
Toyota Smarthome: Real-World Activities of Daily Living	Oct 1, 2019	16kAction Classification	—Unverified
Transfer Learning for Future Wireless Networks: A Comprehensive Survey	Feb 15, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks	Dec 5, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
Transfer Learning in a Transductive Setting	Dec 1, 2013	Activity RecognitionAttribute	—Unverified
Transfer Learning in Human Activity Recognition: A Survey	Jan 18, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Transformer-Based Approaches for Sensor-Based Human Activity Recognition: Opportunities and Challenges	Oct 17, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Transformer-Based Contrastive Meta-Learning For Low-Resource Generalizable Activity Recognition	Dec 28, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Transformers in Vision: A Survey	Jan 4, 2021	Action RecognitionActivity Recognition	—Unverified
Transportation mode recognition based on low-rate acceleration and location signals with an attention-based multiple-instance learning network	Apr 5, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Tri-axial Self-Attention for Concurrent Activity Recognition	Dec 6, 2018	Action DetectionActivity Detection	—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