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
An Information-rich Sampling Technique over Spatio-Temporal CNN for Classification of Human Actions in Videos—0
Bubblenet: A Disperse Recurrent Structure To Recognize Activities—0
BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition—0
A new network-based algorithm for human activity recognition in video—0
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space—0
An Event Calculus Production Rule System for Reasoning in Dynamic and Uncertain Domains—0
Boosted Multiple Kernel Learning for First-Person Activity Recognition—0
Boosted Markov Networks for Activity Recognition—0
A Neurorobotics Approach to Behaviour Selection based on Human Activity Recognition—0
Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging—0
Bonn Activity Maps: Dataset Description—0
BON: An extended public domain dataset for human activity recognition—0
An end-to-end (deep) neural network applied to raw EEG, fNIRs and body motion data for data fusion and BCI classification task without any pre-/post-processing—0
Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer Interaction: A Comprehensive Survey—0
Black-box Attacks on Image Activity Prediction and its Natural Language Explanations—0
An Efficient Data Imputation Technique for Human Activity Recognition—0
Activity recognition using ST-GCN with 3D motion data—0
BiomechGPT: Towards a Biomechanically Fluent Multimodal Foundation Model for Clinically Relevant Motion Tasks—0
Bi-LSTM neural network for EEG-based error detection in musicians' performance—0
Bilinear Programming for Human Activity Recognition with Unknown MRF Graphs—0
Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies—0
Activity Recognition Using A Combination of Category Components And Local Models for Video Surveillance—0
A compressive multi-kernel method for privacy-preserving machine learning—0
A Causality-Aware Pattern Mining Scheme for Group Activity Recognition in a Pervasive Sensor Space—0
Bi-Causal: Group Activity Recognition via Bidirectional Causality—0
Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition—0
Beyond Isolated Frames: Enhancing Sensor-Based Human Activity Recognition through Intra- and Inter-Frame Attention—0
Analysis of Gait Pattern to Recognize the Human Activities—0
Activity Recognition on a Large Scale in Short Videos - Moments in Time Dataset—0
Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets—0
Beyond Actions: Discriminative Models for Contextual Group Activities—0
An adaptable cognitive microcontroller node for fitness activity recognition—0
Benchmark of DNN Model Search at Deployment Time—0
Benchmarking Classical, Deep, and Generative Models for Human Activity Recognition—0
An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity—0
Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs—0
A Comprehensive Review of Automated Data Annotation Techniques in Human Activity Recognition—0
Benchmarking 2D Egocentric Hand Pose Datasets—0
An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson's Disease—0
Batch-Based Activity Recognition from Egocentric Photo-Streams—0
A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation—0
Activity recognition from videos with parallel hypergraph matching on GPUs—0
BAR: Bayesian Activity Recognition using variational inference—0
Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization—0
A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition—0
Balancing Continual Learning and Fine-tuning for Human Activity Recognition—0
Background Knowledge Injection for Interpretable Sequence Classification—0
A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation—0
Activity Recognition From Newborn Resuscitation Videos—0
A Comprehensive Overview on UWB Radar: Applications, Standards, Signal Processing Techniques, Datasets, Radio Chips, Trends and Future Research Directions—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