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 1191–1200 of 1322 papers

Title	Date	Tasks	Status
Human Activity Recognition in an Open World	Dec 23, 2022	Activity RecognitionHuman Activity Recognition	CodeCode Available
Zero-Shot Activity Recognition with Verb Attribute Induction	Jul 29, 2017	Activity RecognitionAttribute	CodeCode Available
Adversarial Attacks on Deep Neural Networks for Time Series Classification	Mar 17, 2019	Activity RecognitionAdversarial Attack	CodeCode Available
Towards Multi-User Activity Recognition through Facilitated Training Data and Deep Learning for Human-Robot Collaboration Applications	Feb 11, 2023	Activity Recognition	CodeCode Available
Situation Recognition: Visual Semantic Role Labeling for Image Understanding	Jun 1, 2016	Activity RecognitionGrounded Situation Recognition	CodeCode Available
Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity Recognition	Sep 27, 2024	Activity RecognitionGraph Learning	CodeCode Available
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes	Jun 12, 2019	Activity RecognitionAudio Classification	CodeCode Available
RWF-2000: An Open Large Scale Video Database for Violence Detection	Nov 14, 2019	Action ClassificationAction Recognition	CodeCode Available
Skeleton-Based Action Recognition with Spatial-Structural Graph Convolution	Jul 31, 2024	Action RecognitionActivity Recognition	CodeCode Available
SALIENCE: An Unsupervised User Adaptation Model for Multiple Wearable Sensors Based Human Activity Recognition	Aug 17, 2021	Activity RecognitionHuman Activity 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