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 231–240 of 1322 papers

Title	Date	Tasks	Status
Boosted Markov Networks for Activity Recognition	Aug 6, 2014	Activity Recognitionfeature selection	—Unverified
Boosted Multiple Kernel Learning for First-Person Activity Recognition	Feb 22, 2017	Activity Recognition	—Unverified
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space	Jul 11, 2024	Action RecognitionActivity Recognition	—Unverified
Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals	Dec 20, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition	Aug 7, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Bubblenet: A Disperse Recurrent Structure To Recognize Activities	Oct 30, 2020	Action RecognitionActivity Recognition	—Unverified
CA3D: Convolutional-Attentional 3D Nets for Efficient Video Activity Recognition on the Edge	May 26, 2025	Activity RecognitionQuantization	—Unverified
CADDI: An in-Class Activity Detection Dataset using IMU data from low-cost sensors	Mar 4, 2025	Action DetectionActivity Detection	—Unverified
An Integrated Approach to Crowd Video Analysis: From Tracking to Multi-level Activity Recognition	Oct 30, 2017	Activity Recognition	—Unverified
A Hybrid Framework for Action Recognition in Low-Quality Video Sequences	Mar 11, 2019	Action RecognitionActivity 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