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 41–50 of 1322 papers

Title	Date	Tasks	Status	Hype
Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity Recognition	May 20, 2022	Activity RecognitionContrastive Learning	CodeCode Available	1
Convolutional Tensor-Train LSTM for Spatio-temporal Learning	Feb 21, 2020	Activity RecognitionVideo Compression	CodeCode Available	1
Audio-Adaptive Activity Recognition Across Video Domains	Mar 27, 2022	Activity RecognitionDomain Adaptation	CodeCode Available	1
Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation	Jun 6, 2020	Activity RecognitionDeep Learning	CodeCode Available	1
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey	Feb 6, 2023	Activity RecognitionClassification	CodeCode Available	1
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing	Nov 7, 2016	Activity RecognitionGeneral Classification	CodeCode Available	1
SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points	Mar 17, 2021	Activity RecognitionBenchmarking	CodeCode Available	1
ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data	Jul 24, 2020	Activity RecognitionChange Point Detection	CodeCode Available	1
Exploring Few-Shot Adaptation for Activity Recognition on Diverse Domains	May 15, 2023	Action RecognitionActivity Recognition	CodeCode Available	1
A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison	Oct 19, 2021	Activity RecognitionFederated 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