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 1161–1170 of 1322 papers

Title	Date	Tasks	Status
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis	Sep 26, 2017	Activity RecognitionEEG	—Unverified
Latent Embeddings for Collective Activity Recognition	Sep 20, 2017	Activity Recognition	—Unverified
Human Activity Recognition Using Robust Adaptive Privileged Probabilistic Learning	Sep 19, 2017	Activity RecognitionHuman Activity Recognition	—Unverified
Kernel Cross-Correlator	Sep 12, 2017	Activity RecognitionHuman Activity Recognition	CodeCode Available
CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations	Sep 11, 2017	Activity Recognitionobject-detection	—Unverified
Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks	Sep 5, 2017	Action RecognitionActivity Recognition	—Unverified
Action Classification and Highlighting in Videos	Aug 31, 2017	Action ClassificationActivity Recognition	—Unverified
Batch-Based Activity Recognition from Egocentric Photo-Streams	Aug 25, 2017	Activity RecognitionOptical Flow Estimation	—Unverified
A Survey of Human Activity Recognition Using WiFi CSI	Aug 23, 2017	Activity RecognitionBIG-bench Machine Learning	CodeCode Available
Activity Recognition based on a Magnitude-Orientation Stream Network	Aug 22, 2017	Activity RecognitionOptical Flow Estimation	—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