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 1091–1100 of 1322 papers

Title	Date	Tasks	Status
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention	Oct 7, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Representation Flow for Action Recognition	Oct 2, 2018	Action ClassificationAction Recognition	CodeCode Available
Arianna+: Scalable Human Activity Recognition by Reasoning with a Network of Ontologies	Sep 21, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Human activity recognition based on time series analysis using U-Net	Sep 20, 2018	Activity RecognitionGesture Recognition	—Unverified
Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence	Sep 11, 2018	Activity RecognitionIntent Detection	—Unverified
Classification of grasping tasks based on EEG-EMG coherence	Sep 10, 2018	Activity RecognitionClassification	—Unverified
On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams	Sep 2, 2018	Action RecognitionActivity Recognition	—Unverified
stagNet: An Attentive Semantic RNN for Group Activity Recognition	Sep 1, 2018	Activity RecognitionGroup Activity Recognition	—Unverified
Activity Recognition on a Large Scale in Short Videos - Moments in Time Dataset	Sep 1, 2018	Action RecognitionActivity Recognition	—Unverified
Hierarchical Relational Networks for Group Activity Recognition and Retrieval	Sep 1, 2018	Activity RecognitionDenoising	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