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 961–970 of 1322 papers

Title	Date	Tasks	Status
Template co-updating in multi-modal human activity recognition systems	Dec 4, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
RSA: Randomized Simulation as Augmentation for Robust Human Action Recognition	Dec 3, 2019	Action RecognitionActivity Recognition	—Unverified
Skeleton based Activity Recognition by Fusing Part-wise Spatio-temporal and Attention Driven Residues	Dec 2, 2019	3D Action RecognitionAction Recognition	—Unverified
Towards Hardware-Aware Tractable Learning of Probabilistic Models	Dec 1, 2019	Activity RecognitionEdge-computing	CodeCode Available
Topological Machine Learning for Multivariate Time Series	Nov 27, 2019	Activity RecognitionBIG-bench Machine Learning	CodeCode Available
Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation	Nov 26, 2019	Activity RecognitionAttribute	CodeCode Available
A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features	Nov 22, 2019	Activity Recognitionintent-classification	—Unverified
Fusion of Deep Neural Networks for Activity Recognition: A Regular Vine Copula Based Approach	Nov 21, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
Simultaneous Implementation Features Extraction and Recognition Using C3D Network for WiFi-based Human Activity Recognition	Nov 21, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
RWF-2000: An Open Large Scale Video Database for Violence Detection	Nov 14, 2019	Action ClassificationAction Recognition	CodeCode Available

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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