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 581–590 of 1322 papers

Title	Date	Tasks	Status	Hype
SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing	Jul 16, 2022	Activity RecognitionDeep Learning	CodeCode Available	2
Multi-Modal Unsupervised Pre-Training for Surgical Operating Room Workflow Analysis	Jul 16, 2022	Activity RecognitionSelf-Supervised Learning	—Unverified	0
Hunting Group Clues with Transformers for Social Group Activity Recognition	Jul 12, 2022	Activity RecognitionGroup Activity Recognition	—Unverified	0
Fine-grained Activities of People Worldwide	Jul 11, 2022	Action DetectionActivity Detection	—Unverified	0
Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity Recognition	Jul 10, 2022	Activity RecognitionContrastive Learning	CodeCode Available	0
Adaptation of Surgical Activity Recognition Models Across Operating Rooms	Jul 7, 2022	Activity RecognitionDomain Adaptation	—Unverified	0
WiFi-based Spatiotemporal Human Action Perception	Jun 20, 2022	Activity RecognitionHuman Activity Recognition	—Unverified	0
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency	Jun 17, 2022	Activity RecognitionDomain Adaptation	CodeCode Available	2
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition	Jun 14, 2022	Activity RecognitionCross-Domain Activity Recognition	—Unverified	0
ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences	Jun 10, 2022	Activity RecognitionPrediction	CodeCode Available	0

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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