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 841–850 of 1322 papers

Title	Date	Tasks	Status
Human Interaction Recognition Framework based on Interacting Body Part Attention	Jan 22, 2021	Activity RecognitionActivity Recognition In Videos	—Unverified
Human-like Relational Models for Activity Recognition in Video	Jul 12, 2021	Activity Recognition	—Unverified
Human Pose Estimation using Motion Priors and Ensemble Models	Jan 26, 2019	2D Human Pose Estimation3D Human Pose Tracking	—Unverified
Hunting Group Clues with Transformers for Social Group Activity Recognition	Jul 12, 2022	Activity RecognitionGroup Activity Recognition	—Unverified
Hybrid Model Featuring CNN and LSTM Architecture for Human Activity Recognition on Smartphone Sensor Data	Dec 5, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
Identifying First-person Camera Wearers in Third-person Videos	Apr 20, 2017	Activity RecognitionObject Tracking	—Unverified
iKAN: Global Incremental Learning with KAN for Human Activity Recognition Across Heterogeneous Datasets	Jun 3, 2024	Activity RecognitionContinual Learning	—Unverified
Image based Eye Gaze Tracking and its Applications	Jul 9, 2019	Activity RecognitionGaze Estimation	—Unverified
iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition	Jan 31, 2024	Activity RecognitionContrastive Learning	—Unverified
Impact of Physical Activity on Sleep:A Deep Learning Based Exploration	Jul 24, 2016	Activity RecognitionDeep Learning	—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