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
Complex Activity Recognition using Granger Constrained DBN (GCDBN) in Sports and Surveillance Video—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
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
Joint segmentation of multivariate time series with hidden process regression 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