SOTAVerified

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 1276–1300 of 1322 papers

TitleStatusHype
A new network-based algorithm for human activity recognition in video—0
A Heat-Map-based Algorithm for Recognizing Group Activities in Videos—0
Discriminative training for Convolved Multiple-Output Gaussian processes—0
Visual Recognition by Counting Instances: A Multi-Instance Cardinality Potential Kernel—0
3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks—0
Pooled Motion Features for First-Person VideosCode0
Multiple object tracking with context awareness—0
Dynamic Programming for Instance Annotation in Multi-instance Multi-label Learning—0
The Evolution of First Person Vision Methods: A Survey—0
Boosted Markov Networks for Activity Recognition—0
See No Evil, Say No Evil: Description Generation from Densely Labeled Images—0
Analysis of Gait Pattern to Recognize the Human Activities—0
Fine-grained Activity Recognition with Holistic and Pose based Features—0
Incremental Activity Modeling and Recognition in Streaming Videos—0
Super Normal Vector for Activity Recognition Using Depth Sequences—0
Rate-Invariant Analysis of Trajectories on Riemannian Manifolds with Application in Visual Speech Recognition—0
Complex Activity Recognition using Granger Constrained DBN (GCDBN) in Sports and Surveillance Video—0
The Sweet-Home speech and multimodal corpus for home automation interaction—0
Indoor Activity Detection and Recognition for Sport Games Analysis—0
Geometry-based Adaptive Symbolic Approximation for Fast Sequence Matching on Manifolds—0
Incremental Learning of Event Definitions with Inductive Logic ProgrammingCode0
Human Activity Recognition using Smartphone—0
Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint—0
Towards Using Unlabeled Data in a Sparse-coding Framework for Human Activity Recognition—0
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression—0
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All datasetsRWF-2000Stanford40First-Person Hand Action BenchmarkSelf-stimulatory Behavior Dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Structured Keypoint PoolingAccuracy93.4—Unverified
2Semi-Supervised Hard Attention (SSHA); pretrained on Deepmind Kinetics datasetAccuracy90.4—Unverified
3Human Skeletons + Change DetectionAccuracy90.25—Unverified
4Separable Convolutional LSTMAccuracy89.75—Unverified
5SPIL ConvolutionAccuracy89.3—Unverified
6Flow Gated NetworkAccuracy87.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FocusCLIPTop-3 Accuracy (%)10.47—Unverified
2CLIPTop-3 Accuracy (%)6.49—Unverified
#ModelMetricClaimedVerifiedStatus
1Boutaleb et al.1:1 Accuracy97.91—Unverified
#ModelMetricClaimedVerifiedStatus
1all-landmark-modelActivity Recognition0.76—Unverified