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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1001–1025 of 1322 papers

TitleStatusHype
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare—0
Automated Surgical Activity Recognition with One Labeled Sequence—0
Joint Activity Recognition and Indoor Localization With WiFi FingerprintsCode0
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
AVD: Adversarial Video Distillation—0
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence ModelCode0
Tweets Can Tell: Activity Recognition using Hybrid Long Short-Term Memory Model—0
Image based Eye Gaze Tracking and its Applications—0
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
Human Activity Recognition with Convolutional Neural NetowrksCode0
Automated Activity Recognition of Construction Equipment Using a Data Fusion Approach—0
PI-Net: A Deep Learning Approach to Extract Topological Persistence ImagesCode0
ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity RecognitionCode0
Personalizing human activity recognition models using incremental learning—0
From User-independent to Personal Human Activity Recognition Models Exploiting the Sensors of a Smartphone—0
Importance of user inputs while using incremental learning to personalize human activity recognition models—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