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 81–90 of 1322 papers

Title	Date	Tasks	Status	Hype
Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning	Sep 23, 2023	Activity RecognitionContrastive Learning	CodeCode Available	1
Fine-Grained Egocentric Hand-Object Segmentation: Dataset, Model, and Applications	Aug 7, 2022	Activity RecognitionData Augmentation	CodeCode Available	1
Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos	Mar 26, 2022	Action SegmentationAction Understanding	CodeCode Available	1
Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient	Aug 10, 2023	Action RecognitionActivity Recognition	CodeCode Available	1
HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data	Mar 7, 2022	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
HHAR-net: Hierarchical Human Activity Recognition using Neural Networks	Oct 28, 2020	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark	Jan 5, 2021	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
Human Activity Segmentation Challenge @ ECML/PKDD’23	Sep 18, 2023	Activity RecognitionChange Point Detection	CodeCode Available	1
Improving Deep Learning for HAR with shallow LSTMs	Aug 2, 2021	Activity RecognitionDeep Learning	CodeCode Available	1
Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity Recognition	May 20, 2022	Activity RecognitionContrastive Learning	CodeCode Available	1

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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