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 651–660 of 1322 papers

Title	Date	Tasks	Status
Multi-stage RGB-based Transfer Learning Pipeline for Hand Activity Recognition	Feb 8, 2022	Activity RecognitionTransfer Learning	—Unverified
Video Violence Recognition and Localization Using a Semi-Supervised Hard Attention Model	Feb 4, 2022	Activity RecognitionHard Attention	—Unverified
Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects	Feb 2, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
DIAT-μ RadHAR (micro-doppler signature dataset) & μ RadNet (a lightweight DCNN)—For human suspicious activity recognition	Feb 1, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs	Feb 1, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition	Feb 1, 2022	Activity RecognitionContrastive Learning	—Unverified
Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning	Jan 21, 2022	Activity RecognitionBIG-bench Machine Learning	—Unverified
Physical Activity Recognition by Utilising Smartphone Sensor Signals	Jan 20, 2022	Activity RecognitionComputer Security	—Unverified
WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data	Jan 18, 2022	Activity RecognitionChange Point Detection	—Unverified
Homogenization of Existing Inertial-Based Datasets to Support Human Activity Recognition	Jan 17, 2022	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