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 551–560 of 1322 papers

Title	Date	Tasks	Status
VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wild	Mar 3, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
EdgeServe: A Streaming System for Decentralized Model Serving	Mar 2, 2023	Activity RecognitionAutonomous Driving	—Unverified
Knowledge Augmented Relation Inference for Group Activity Recognition	Feb 28, 2023	Activity RecognitionGroup Activity Recognition	—Unverified
Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism	Feb 27, 2023	Activity RecognitionAnomaly Detection	—Unverified
A Preliminary Study on Pattern Reconstruction for Optimal Storage of Wearable Sensor Data	Feb 25, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
FG-SSA: Features Gradient-based Signals Selection Algorithm of Linear Complexity for Convolutional Neural Networks	Feb 23, 2023	Activity RecognitionClassification	—Unverified
Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition	Feb 21, 2023	Activity Recognition	—Unverified
On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge	Feb 18, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
cGAN-Based High Dimensional IMU Sensor Data Generation for Enhanced Human Activity Recognition in Therapeutic Activities	Feb 16, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Towards Multi-User Activity Recognition through Facilitated Training Data and Deep Learning for Human-Robot Collaboration Applications	Feb 11, 2023	Activity Recognition	CodeCode Available

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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