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 1241–1250 of 1322 papers

Title	Date	Tasks	Status
Leaving Some Stones Unturned: Dynamic Feature Prioritization for Activity Detection in Streaming Video	Apr 1, 2016	Action DetectionActivity Detection	—Unverified
Temporally Robust Global Motion Compensation by Keypoint-based Congealing	Mar 12, 2016	Activity RecognitionHuman Activity Recognition	—Unverified
Learning from Imbalanced Multiclass Sequential Data Streams Using Dynamically Weighted Conditional Random Fields	Mar 11, 2016	Activity RecognitionGeneral Classification	—Unverified
Mobile Big Data Analytics Using Deep Learning and Apache Spark	Feb 23, 2016	Activity RecognitionDeep Learning	—Unverified
Learning Privately from Multiparty Data	Feb 10, 2016	Activity RecognitionIntrusion Detection	—Unverified
Appearance Based Robot and Human Activity Recognition System	Feb 4, 2016	Activity RecognitionHuman Activity Recognition	—Unverified
Shape Distributions of Nonlinear Dynamical Systems for Video-based Inference	Jan 27, 2016	Activity RecognitionGesture Recognition	—Unverified
Egocentric Activity Recognition with Multimodal Fisher Vector	Jan 25, 2016	Activity RecognitionEgocentric Activity Recognition	—Unverified
Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition	Dec 21, 2015	Activity RecognitionDynamic Time Warping	—Unverified
An Event Calculus Production Rule System for Reasoning in Dynamic and Uncertain Domains	Dec 14, 2015	Activity RecognitionEpistemic Reasoning	—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