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 1001–1010 of 1322 papers

Title	Date	Tasks	Status
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare	Jul 22, 2019	Activity RecognitionFederated Learning	—Unverified
Automated Surgical Activity Recognition with One Labeled Sequence	Jul 20, 2019	Activity Recognition	—Unverified
Joint Activity Recognition and Indoor Localization With WiFi Fingerprints	Jul 18, 2019	Activity RecognitionIndoor Localization	CodeCode Available
An end-to-end (deep) neural network applied to raw EEG, fNIRs and body motion data for data fusion and BCI classification task without any pre-/post-processing	Jul 17, 2019	Action DetectionActivity Recognition	—Unverified
AVD: Adversarial Video Distillation	Jul 12, 2019	Activity RecognitionDecoder	—Unverified
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence Model	Jul 12, 2019	Activity RecognitionFeature Engineering	CodeCode Available
Tweets Can Tell: Activity Recognition using Hybrid Long Short-Term Memory Model	Jul 10, 2019	Activity Recognition	—Unverified
Image based Eye Gaze Tracking and its Applications	Jul 9, 2019	Activity RecognitionGaze Estimation	—Unverified
Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification	Jul 7, 2019	Activity RecognitionBIG-bench Machine Learning	—Unverified
Resource-Efficient Computing in Wearable Systems	Jul 7, 2019	Activity RecognitionGeneral Classification	—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