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 1041–1050 of 1322 papers

Title	Date	Tasks	Status
Learning Actor Relation Graphs for Group Activity Recognition	Apr 23, 2019	Action RecognitionActivity Recognition	CodeCode Available
Latent Variable Algorithms for Multimodal Learning and Sensor Fusion	Apr 23, 2019	Activity RecognitionDecision Making	—Unverified
Semi-Supervised First-Person Activity Recognition in Body-Worn Video	Apr 19, 2019	Activity Recognition	—Unverified
Smart Laptop Bag with Machine Learning for Activity Recognition	Apr 14, 2019	Activity RecognitionBIG-bench Machine Learning	—Unverified
Unsupervised Synthesis of Anomalies in Videos: Transforming the Normal	Apr 14, 2019	Activity RecognitionData Augmentation	—Unverified
Digging Deeper into Egocentric Gaze Prediction	Apr 12, 2019	Activity RecognitionGaze Prediction	—Unverified
Context-Aware Query Selection for Active Learning in Event Recognition	Apr 9, 2019	Active LearningActivity Recognition	—Unverified
Convolutional Relational Machine for Group Activity Recognition	Apr 5, 2019	Activity RecognitionGroup Activity Recognition	—Unverified
Subject Cross Validation in Human Activity Recognition	Apr 4, 2019	Activity RecognitionHuman Activity Recognition	CodeCode Available
Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks	Mar 29, 2019	Activity RecognitionGenerative Adversarial Network	—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