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 831–840 of 1322 papers

Title	Date	Tasks	Status
Anomaly detection and regime searching in fitness-tracker data	Jan 1, 2021	Activity RecognitionAnomaly Detection	—Unverified
Sequence Metric Learning as Synchronization of Recurrent Neural Networks	Jan 1, 2021	Activity RecognitionMetric Learning	—Unverified
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition	Jan 1, 2021	Activity RecognitionCombinatorial Optimization	—Unverified
Deep Positive Unlabeled Learning with a Sequential Bias	Jan 1, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
3D Human motion anticipation and classification	Dec 31, 2020	Action RecognitionActivity Recognition	—Unverified
Invariant Feature Learning for Sensor-based Human Activity Recognition	Dec 14, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring	Dec 14, 2020	Activity RecognitionFederated Learning	—Unverified
T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis	Dec 10, 2020	Activity RecognitionBrain Computer Interface	—Unverified
Contrastive Predictive Coding for Human Activity Recognition	Dec 9, 2020	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

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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