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 1081–1090 of 1322 papers

Title	Date	Tasks	Status
Combined Static and Motion Features for Deep-Networks Based Activity Recognition in Videos	Oct 16, 2018	Activity RecognitionActivity Recognition In Videos	—Unverified
Ubicoustics: Plug-and-Play Acoustic Activity Recognition	Oct 14, 2018	Activity RecognitionAudio Classification	CodeCode Available
Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity	Oct 12, 2018	Activity RecognitionBenchmarking	CodeCode Available
Optimized Gated Deep Learning Architectures for Sensor Fusion	Oct 8, 2018	Activity RecognitionAutonomous Driving	—Unverified
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention	Oct 7, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Representation Flow for Action Recognition	Oct 2, 2018	Action ClassificationAction Recognition	CodeCode Available
Arianna+: Scalable Human Activity Recognition by Reasoning with a Network of Ontologies	Sep 21, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Human activity recognition based on time series analysis using U-Net	Sep 20, 2018	Activity RecognitionGesture Recognition	—Unverified
Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence	Sep 11, 2018	Activity RecognitionIntent Detection	—Unverified
Classification of grasping tasks based on EEG-EMG coherence	Sep 10, 2018	Activity RecognitionClassification	—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