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 411–420 of 1322 papers

Title	Date	Tasks	Status
Cheating off your neighbors: Improving activity recognition through corroboration	May 27, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks	May 26, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning	May 25, 2023	Activity RecognitionContrastive Learning	—Unverified
CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy Convolution Neural Network	May 24, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Large Language Models are Few-Shot Health Learners	May 24, 2023	Activity RecognitionLanguage Modeling	—Unverified
ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition	May 22, 2023	Activity RecognitionData Augmentation	CodeCode Available
FieldHAR: A Fully Integrated End-to-end RTL Framework for Human Activity Recognition with Neural Networks from Heterogeneous Sensors	May 22, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-Worn Inertial Sensors	May 22, 2023	Activity RecognitionHuman Activity Recognition	CodeCode Available
WiFi-TCN: Temporal Convolution for Human Interaction Recognition based on WiFi signal	May 21, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Real-time Aerial Detection and Reasoning on Embedded-UAVs	May 21, 2023	Activity RecognitionPedestrian Detection	—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