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 1121–1130 of 1322 papers

Title	Date	Tasks	Status
Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling	May 17, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Object and Text-guided Semantics for CNN-based Activity Recognition	May 4, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Human Activity Recognition using Recurrent Neural Networks	Apr 19, 2018	Activity RecognitionBIG-bench Machine Learning	—Unverified
M-PACT: An Open Source Platform for Repeatable Activity Classification Research	Apr 16, 2018	Action ClassificationActivity Recognition	CodeCode Available
Fine-grained Activity Recognition in Baseball Videos	Apr 9, 2018	Action DetectionActivity Detection	CodeCode Available
Question Type Guided Attention in Visual Question Answering	Apr 6, 2018	Activity RecognitionQuestion Answering	—Unverified
Non-Linear Temporal Subspace Representations for Activity Recognition	Mar 27, 2018	Action RecognitionActivity Recognition	—Unverified
Modelling the Influence of Cultural Information on Vision-Based Human Home Activity Recognition	Mar 21, 2018	Activity RecognitionCultural Vocal Bursts Intensity Prediction	—Unverified
Dynamic Vision Sensors for Human Activity Recognition	Mar 13, 2018	Activity RecognitionHuman Activity Recognition	CodeCode Available
Analysis of Hand Segmentation in the Wild	Mar 8, 2018	Action RecognitionActivity Recognition	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