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 861–870 of 1322 papers

Title	Date	Tasks	Status	Hype
Towards Deep Clustering of Human Activities from Wearables	Aug 2, 2020	Activity RecognitionClustering	—Unverified	0
Human Interaction Learning on 3D Skeleton Point Clouds for Video Violence Recognition	Aug 1, 2020	Activity Recognition	—Unverified	0
Empowering Relational Network by Self-Attention Augmented Conditional Random Fields for Group Activity Recognition	Aug 1, 2020	Activity RecognitionGroup Activity Recognition	—Unverified	0
AssembleNet++: Assembling Modality Representations via Attention Connections - Supplementary Material -	Aug 1, 2020	Activity Recognition	—Unverified	0
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning	Jul 27, 2020	3D Human Pose Estimation3D Shape Reconstruction	CodeCode Available	1
ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data	Jul 24, 2020	Activity RecognitionChange Point Detection	CodeCode Available	1
Directional Temporal Modeling for Action Recognition	Jul 21, 2020	Action RecognitionActivity Recognition	—Unverified	0
Creating a Large-scale Synthetic Dataset for Human Activity Recognition	Jul 21, 2020	Activity RecognitionHuman Activity Recognition	—Unverified	0
Social Adaptive Module for Weakly-supervised Group Activity Recognition	Jul 18, 2020	Activity RecognitionGroup Activity Recognition	—Unverified	0
Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors	Jul 14, 2020	Activity RecognitionHuman Activity Recognition	—Unverified	0

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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