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 351–360 of 1322 papers

Title	Date	Tasks	Status
An Information-rich Sampling Technique over Spatio-Temporal CNN for Classification of Human Actions in Videos	Feb 6, 2020	Action RecognitionAction Recognition In Videos	—Unverified
Bubblenet: A Disperse Recurrent Structure To Recognize Activities	Oct 30, 2020	Action RecognitionActivity Recognition	—Unverified
BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition	Aug 7, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
A new network-based algorithm for human activity recognition in video	Feb 21, 2015	Action DetectionActivity Detection	—Unverified
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space	Jul 11, 2024	Action RecognitionActivity Recognition	—Unverified
An Event Calculus Production Rule System for Reasoning in Dynamic and Uncertain Domains	Dec 14, 2015	Activity RecognitionEpistemic Reasoning	—Unverified
Boosted Multiple Kernel Learning for First-Person Activity Recognition	Feb 22, 2017	Activity Recognition	—Unverified
Boosted Markov Networks for Activity Recognition	Aug 6, 2014	Activity Recognitionfeature selection	—Unverified
A Neurorobotics Approach to Behaviour Selection based on Human Activity Recognition	Jul 27, 2021	Activity RecognitionDecision Making	—Unverified
Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging	Jan 10, 2021	Action DetectionActivity Detection	—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