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 1271–1280 of 1322 papers

Title	Date	Tasks	Status
Compact CNN for Indexing Egocentric Videos	Apr 28, 2015	Activity RecognitionOptical Flow Estimation	—Unverified
Detecting Falls with X-Factor Hidden Markov Models	Apr 8, 2015	Activity RecognitionGeneral Classification	—Unverified
Latent Hierarchical Model for Activity Recognition	Mar 6, 2015	Activity RecognitionHuman Activity Recognition	—Unverified
Activity Recognition Using A Combination of Category Components And Local Models for Video Surveillance	Feb 28, 2015	Activity Recognition	—Unverified
Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Data	Feb 23, 2015	Activity Recognition	—Unverified
A new network-based algorithm for human activity recognition in video	Feb 21, 2015	Action DetectionActivity Detection	—Unverified
A Heat-Map-based Algorithm for Recognizing Group Activities in Videos	Feb 21, 2015	Activity RecognitionGroup Activity Recognition	—Unverified
Discriminative training for Convolved Multiple-Output Gaussian processes	Feb 7, 2015	Activity RecognitionEmotion Recognition	—Unverified
Visual Recognition by Counting Instances: A Multi-Instance Cardinality Potential Kernel	Feb 6, 2015	Activity RecognitionEvent Detection	—Unverified
3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks	Jan 26, 2015	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