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

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Showing 841–850 of 1322 papers

Title	Date	Tasks	Status
Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring	Dec 4, 2020	Activity RecognitionBIG-bench Machine Learning	—Unverified
MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection	Dec 2, 2020	Action DetectionActivity Detection	—Unverified
Fine-grained activity recognition for assembly videos	Dec 2, 2020	Action RecognitionActivity Recognition	—Unverified
Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes	Dec 1, 2020	Activity RecognitionGeneral Classification	CodeCode Available
A compact sequence encoding scheme for online human activity recognition in HRI applications	Dec 1, 2020	Action RecognitionActivity Recognition	—Unverified
Post-training Iterative Hierarchical Data Augmentation for Deep Networks	Dec 1, 2020	Activity RecognitionData Augmentation	—Unverified
LaHAR: Latent Human Activity Recognition using LDA	Nov 23, 2020	Activity RecognitionClustering	CodeCode Available
Yet it moves: Learning from Generic Motions to Generate IMU data from YouTube videos	Nov 23, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
Self-Supervised Transformers for Activity Classification using Ambient Sensors	Nov 22, 2020	Activity RecognitionAnomaly Detection	—Unverified
Human activity recognition using improved dynamic image	Nov 15, 2020	Action RecognitionActivity Recognition	—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