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 541–550 of 1322 papers

Title	Date	Tasks	Status	Hype
Low-Resolution Action Recognition for Tiny Actions Challenge	Sep 28, 2022	Action RecognitionActivity Recognition	—Unverified	0
DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness	Sep 26, 2022	Activity RecognitionDenoising	—Unverified	0
Heterogeneous Recurrent Spiking Neural Network for Spatio-Temporal Classification	Sep 22, 2022	Activity RecognitionClassification	—Unverified	0
Lightweight Transformers for Human Activity Recognition on Mobile Devices	Sep 22, 2022	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
Contrastive Learning for Time Series on Dynamic Graphs	Sep 21, 2022	Activity RecognitionAnomaly Detection	—Unverified	0
An Overview of Violence Detection Techniques: Current Challenges and Future Directions	Sep 21, 2022	Activity Recognition	—Unverified	0
Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition	Sep 15, 2022	Action RecognitionActivity Recognition	—Unverified	0
Out-of-Distribution Representation Learning for Time Series Classification	Sep 15, 2022	Activity RecognitionClassification	—Unverified	0
TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation	Sep 14, 2022	Activity RecognitionHuman Activity Recognition	—Unverified	0
BON: An extended public domain dataset for human activity recognition	Sep 12, 2022	Activity RecognitionHuman Activity Recognition	—Unverified	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