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 1211–1220 of 1322 papers

Title	Date	Tasks	Status	Hype
Jointly learning heterogeneous features for rgb-d activity recognition	Dec 15, 2016	Activity RecognitionBenchmarking	—Unverified	0
Unsupervised Human Action Detection by Action Matching	Dec 2, 2016	Action DetectionActivity Recognition	—Unverified	0
Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition	Nov 28, 2016	Action LocalizationAction Recognition	—Unverified	0
UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones	Nov 23, 2016	Activity RecognitionGeneral Classification	—Unverified	0
Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation	Nov 17, 2016	Action RecognitionActivity Recognition	—Unverified	0
Answering Image Riddles using Vision and Reasoning through Probabilistic Soft Logic	Nov 17, 2016	Activity RecognitionQuestion Answering	—Unverified	0
Deep Recurrent Neural Network for Mobile Human Activity Recognition with High Throughput	Nov 11, 2016	Activity RecognitionHuman Activity Recognition	—Unverified	0
Action2Activity: Recognizing Complex Activities from Sensor Data	Nov 7, 2016	Action RecognitionActivity Recognition	—Unverified	0
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing	Nov 7, 2016	Activity RecognitionGeneral Classification	CodeCode Available	1
Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders	Oct 12, 2016	Activity RecognitionOne-Class Classification	—Unverified	0

Show:10 25 50

← PrevPage 122 of 133Next →

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