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 1231–1240 of 1322 papers

Title	Date	Tasks	Status
Untrimmed Video Classification for Activity Detection: submission to ActivityNet Challenge	Jul 7, 2016	Action DetectionActivity Detection	CodeCode Available
Laplacian LRR on Product Grassmann Manifolds for Human Activity Clustering in Multi-Camera Video Surveillance	Jun 13, 2016	Activity RecognitionClustering	—Unverified
Situation Recognition: Visual Semantic Role Labeling for Image Understanding	Jun 1, 2016	Activity RecognitionGrounded Situation Recognition	CodeCode Available
Discriminative Hierarchical Rank Pooling for Activity Recognition	Jun 1, 2016	Action RecognitionActivity Recognition	—Unverified
First Person Action Recognition Using Deep Learned Descriptors	Jun 1, 2016	Action RecognitionActivity Recognition	—Unverified
Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters	May 26, 2016	Action ClassificationAction Recognition In Videos	CodeCode Available
Going Deeper into First-Person Activity Recognition	May 12, 2016	Action RecognitionActivity Recognition	—Unverified
Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables	Apr 29, 2016	Activity RecognitionHuman Activity Recognition	—Unverified
Attributes for Improved Attributes: A Multi-Task Network for Attribute Classification	Apr 25, 2016	Activity RecognitionAttribute	—Unverified
Privacy-Preserving Human Activity Recognition from Extreme Low Resolution	Apr 12, 2016	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