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 251–275 of 1322 papers

TitleStatusHype
Understanding Human Activity with Uncertainty Measure for Novelty in Graph Convolutional Networks—0
Generalizable Indoor Human Activity Recognition Method Based on Micro-Doppler Corner Point Cloud and Dynamic Graph Learning—0
WearableMil: An End-to-End Framework for Military Activity Recognition and Performance Monitoring—0
WiDistill: Distilling Large-scale Wi-Fi Datasets with Trajectory MatchingCode0
Does SpatioTemporal information benefit Two video summarization benchmarks?Code0
TRIS-HAR: Transmissive Reconfigurable Intelligent Surfaces-assisted Cognitive Wireless Human Activity Recognition Using State Space Models—0
Plots Unlock Time-Series Understanding in Multimodal Models—0
Deep Adversarial Learning with Activity-Based User Discrimination Task for Human Activity Recognition—0
Ranking the Top-K Realizations of Stochastically Known Event Logs—0
VecLSTM: Trajectory Data Processing and Management for Activity Recognition through LSTM Vectorization and Database Integration—0
Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity RecognitionCode0
EAGLE: Egocentric AGgregated Language-video Engine—0
Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognition—0
Non-stationary BERT: Exploring Augmented IMU Data For Robust Human Activity Recognition—0
Towards Child-Inclusive Clinical Video Understanding for Autism Spectrum Disorder—0
Context-Aware Predictive Coding: A Representation Learning Framework for WiFi SensingCode0
Context-Aware Predictive Coding: A Representation Learning Framework for WiFi SensingCode0
Multidimensional Human Activity Recognition With Large Language Model: A Conceptual Framework—0
Integrating Audio Narrations to Strengthen Domain Generalization in Multimodal First-Person Action Recognition—0
A Comprehensive Methodological Survey of Human Activity Recognition Across Divers Data Modalities—0
Language-centered Human Activity Recognition—0
Benchmarking 2D Egocentric Hand Pose Datasets—0
A Wearable Multi-Modal Edge-Computing System for Real-Time Kitchen Activity Recognition—0
Few-Shot Continual Learning for Activity Recognition in Classroom Surveillance Images—0
Unified Framework with Consistency across Modalities for Human Activity RecognitionCode0
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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