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 351–375 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
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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