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 1001–1025 of 1322 papers

TitleStatusHype
Resource-Efficient Wearable Computing for Real-Time Reconfigurable Machine Learning: A Cascading Binary Classification—0
Resource-Efficient Computing in Wearable Systems—0
Novel evaluation of surgical activity recognition models using task-based efficiency metrics—0
Human Body Parts Tracking: Applications to Activity Recognition—0
A Framework For Identifying Group Behavior Of Wild Animals—0
An IoT Based Framework For Activity Recognition Using Deep Learning TechniqueCode0
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical BayesCode0
Different Approaches for Human Activity Recognition: A Survey—0
Context-driven Active and Incremental Activity Recognition—0
SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks—0
Automated Activity Recognition of Construction Equipment Using a Data Fusion Approach—0
Human Activity Recognition with Convolutional Neural NetowrksCode0
PI-Net: A Deep Learning Approach to Extract Topological Persistence ImagesCode0
ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity RecognitionCode0
From User-independent to Personal Human Activity Recognition Models Exploiting the Sensors of a Smartphone—0
Personalizing human activity recognition models using incremental learning—0
Importance of user inputs while using incremental learning to personalize human activity recognition models—0
Multi-agent Attentional Activity Recognition—0
Activity Recognition and Prediction in Real Homes—0
Disparity-Augmented Trajectories for Human Activity Recognition—0
Federated Multi-task Hierarchical Attention Model for Sensor Analytics—0
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity UnderstandingCode1
On Flow Profile Image for Video Representation—0
Follow the Attention: Combining Partial Pose and Object Motion for Fine-Grained Action Detection—0
Differential Recurrent Neural Network and its Application for Human Activity Recognition—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