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

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Showing 271–280 of 1322 papers

Title	Date	Tasks	Status
A Symbolic Representation of Human Posture for Interpretable Learning and Reasoning	Oct 17, 2022	Activity RecognitionSpatial Reasoning	—Unverified
AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments	Jun 13, 2025	Activity RecognitionHuman Activity Recognition	—Unverified
Convolutional Relational Machine for Group Activity Recognition	Apr 5, 2019	Activity RecognitionGroup Activity Recognition	—Unverified
A Survey on Multi-Resident Activity Recognition in Smart Environments	Apr 24, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
A Survey on Multimodal Wearable Sensor-based Human Action Recognition	Apr 14, 2024	Action RecognitionActivity Recognition	—Unverified
A framework for mining process models from emails logs	Sep 20, 2016	Activity Recognition	—Unverified
A Survey on Human-aware Robot Navigation	Jun 22, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
A Survey of Knowledge Representation in Service Robotics	Jul 5, 2018	Activity RecognitionBIG-bench Machine Learning	—Unverified
A Framework For Identifying Group Behavior Of Wild Animals	Jul 1, 2019	Activity Recognition	—Unverified
Activity-Aware Deep Cognitive Fatigue Assessment using Wearables	May 5, 2021	Activity 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