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 1171–1180 of 1322 papers

Title	Date	Tasks	Status
Hierarchical Deep Temporal Models for Group Activity Recognition	Jul 9, 2016	Activity RecognitionGroup Activity Recognition	CodeCode Available
Hierarchical Relational Networks for Group Activity Recognition and Retrieval	Sep 1, 2018	Activity RecognitionDenoising	CodeCode Available
Evaluating Spiking Neural Network On Neuromorphic Platform For Human Activity Recognition	Aug 1, 2023	Activity RecognitionHuman Activity Recognition	CodeCode Available
Hierarchical Temporal Convolution Network:Towards Privacy-Centric Activity Recognition	Dec 21, 2024	Activity RecognitionSkeleton Based Action Recognition	CodeCode Available
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors	Aug 22, 2017	Activity RecognitionHuman Activity Recognition	CodeCode Available
Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation	Nov 26, 2019	Activity RecognitionAttribute	CodeCode Available
Mindful Active Learning	Jul 28, 2019	Active LearningActivity Recognition	CodeCode Available
Unsupervised Deep Learning-based clustering for Human Activity Recognition	Nov 10, 2022	Activity RecognitionClustering	CodeCode Available
Robotic Vision and Multi-View Synergy: Action and activity recognition in assisted living scenarios	Sep 1, 2024	Activity RecognitionHuman Activity Recognition	CodeCode Available
Towards Hardware-Aware Tractable Learning of Probabilistic Models	Dec 1, 2019	Activity RecognitionEdge-computing	CodeCode Available

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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