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	Score
DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks	Mar 23, 2020	Activity RecognitionAttribute	CodeCode Available	5
Generalized Relevance Learning Grassmann Quantization	Mar 14, 2024	Activity RecognitionFace Recognition	CodeCode Available	5
AssembleNet++: Assembling Modality Representations via Attention Connections	Aug 18, 2020	Action ClassificationActivity Recognition	CodeCode Available	5
ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity Recognition	May 18, 2021	Activity RecognitionHuman Activity Recognition	CodeCode Available	5
Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred Learning	Aug 6, 2024	Activity RecognitionDenoising	CodeCode Available	5
Non-Uniform Subset Selection for Active Learning in Structured Data	Jun 1, 2017	Active LearningActivity Recognition	CodeCode Available	5
Object Level Visual Reasoning in Videos	Jun 16, 2018	Activity RecognitionHuman Activity Recognition	CodeCode Available	5
Online Learning of Event Definitions	Jul 30, 2016	Activity RecognitionInductive logic programming	CodeCode Available	5
DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers	Mar 14, 2024	Activity RecognitionDiversity	CodeCode Available	5
Does SpatioTemporal information benefit Two video summarization benchmarks?	Oct 4, 2024	Activity RecognitionVideo Summarization	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