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
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
Novel evaluation of surgical activity recognition models using task-based efficiency metrics	Jul 3, 2019	Activity Recognition	—Unverified
Human Body Parts Tracking: Applications to Activity Recognition	Jul 2, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
A Framework For Identifying Group Behavior Of Wild Animals	Jul 1, 2019	Activity Recognition	—Unverified
An IoT Based Framework For Activity Recognition Using Deep Learning Technique	Jun 17, 2019	Activity RecognitionHuman Activity Recognition	CodeCode Available
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes	Jun 12, 2019	Activity RecognitionAudio Classification	CodeCode Available
Different Approaches for Human Activity Recognition: A Survey	Jun 11, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
Context-driven Active and Incremental Activity Recognition	Jun 7, 2019	Active LearningActivity Recognition	—Unverified
SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks	Jun 6, 2019	Activity RecognitionHuman Activity 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