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 351–400 of 1322 papers

TitleStatusHype
Cross-Country Skiing Gears Classification using Deep Learning—0
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances—0
Deep Positive Unlabeled Learning with a Sequential Bias—0
Deep Recurrent Neural Network for Mobile Human Activity Recognition with High Throughput—0
A Transformer-Based Model for the Prediction of Human Gaze Behavior on Videos—0
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator—0
Deep Structured Models For Group Activity Recognition—0
Deep Transfer Learning for Cross-domain Activity Recognition—0
A Review of Machine Learning Methods Applied to Video Analysis Systems—0
Creating a Large-scale Synthetic Dataset for Human Activity Recognition—0
Post-train Black-box Defense via Bayesian Boundary Correction—0
Attention-based Convolutional Neural Network for Weakly Labeled Human Activities Recognition with Wearable Sensors—0
CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition—0
Description of Structural Biases and Associated Data in Sensor-Rich Environments—0
Design and Analysis of Efficient Attention in Transformers for Social Group Activity Recognition—0
DeSPITE: Exploring Contrastive Deep Skeleton-Pointcloud-IMU-Text Embeddings for Advanced Point Cloud Human Activity Understanding—0
Detecting Falls with X-Factor Hidden Markov Models—0
Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence—0
Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders—0
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications—0
Detector-Free Weakly Supervised Group Activity Recognition—0
Device-Free Human State Estimation using UWB Multi-Static Radios—0
DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition—0
DGAR: A Unified Domain Generalization Framework for RF-Enabled Human Activity Recognition—0
Action Segmentation Using 2D Skeleton Heatmaps and Multi-Modality Fusion—0
Different Approaches for Human Activity Recognition: A Survey—0
Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition—0
Differentially Private 2D Human Pose Estimation—0
Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition—0
Differentially Private Video Activity Recognition—0
Digging Deeper into Egocentric Gaze Prediction—0
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding—0
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition—0
EAGLE: Egocentric AGgregated Language-video Engine—0
Convolutional Relational Machine for Group Activity Recognition—0
Discriminating sensor activation in activity recognition within multi-occupancy environments based on nearby interaction—0
A Masked Semi-Supervised Learning Approach for Otago Micro Labels Recognition—0
Discriminative Hierarchical Rank Pooling for Activity Recognition—0
Automated Activity Recognition in Clinical Documents—0
Discriminative training for Convolved Multiple-Output Gaussian processes—0
Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Data—0
Disparity-Augmented Trajectories for Human Activity Recognition—0
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition—0
Distributed Agent-Based Collaborative Learning in Cross-Individual Wearable Sensor-Based Human Activity Recognition—0
Automated Level Crossing System: A Computer Vision Based Approach with Raspberry Pi Microcontroller—0
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing—0
Distribution estimation and change-point estimation for time series via DNN-based GANs—0
Automated Surgical Activity Recognition with One Labeled Sequence—0
Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition—0
A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based 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