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 241–250 of 1322 papers

Title	Date	Tasks	Status	Score
MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained Devices	May 15, 2025	Activity RecognitionCode Generation	CodeCode Available	5
DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers	Mar 14, 2024	Activity RecognitionDiversity	CodeCode Available	5
Adversarial Attacks on Deep Neural Networks for Time Series Classification	Mar 17, 2019	Activity RecognitionAdversarial Attack	CodeCode Available	5
PI-Net: A Deep Learning Approach to Extract Topological Persistence Images	Jun 5, 2019	Activity RecognitionDeep Learning	CodeCode Available	5
Pose And Joint-Aware Action Recognition	Oct 16, 2020	Action ClassificationAction Recognition	CodeCode Available	5
PoseTrack: A Benchmark for Human Pose Estimation and Tracking	Oct 27, 2017	Activity RecognitionMulti-Person Pose Estimation	CodeCode Available	5
Discriminatively Learned Hierarchical Rank Pooling Networks	May 30, 2017	Activity RecognitionBilevel Optimization	CodeCode Available	5
Does SpatioTemporal information benefit Two video summarization benchmarks?	Oct 4, 2024	Activity RecognitionVideo Summarization	CodeCode Available	5
An IoT Based Framework For Activity Recognition Using Deep Learning Technique	Jun 17, 2019	Activity RecognitionHuman Activity Recognition	CodeCode Available	5
Dynamic Vision Sensors for Human Activity Recognition	Mar 13, 2018	Activity RecognitionHuman Activity Recognition	CodeCode Available	5

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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