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 526–550 of 1322 papers

TitleStatusHype
Differential Recurrent Neural Network and its Application for Human 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
Differentially Private Video Activity Recognition—0
Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition—0
A Logic Programming Approach to Activity Recognition—0
Differentially Private 2D Human Pose Estimation—0
Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition—0
Different Approaches for Human Activity Recognition: A Survey—0
DIAT-μ RadHAR (micro-doppler signature dataset) & μ RadNet (a lightweight DCNN)—For human suspicious activity recognition—0
Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning—0
DGAR: A Unified Domain Generalization Framework for RF-Enabled Human Activity Recognition—0
DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition—0
Attributes for Improved Attributes: A Multi-Task Network for Attribute Classification—0
Device-Free Human State Estimation using UWB Multi-Static Radios—0
Detector-Free Weakly Supervised Group Activity Recognition—0
Attentive pooling for Group Activity Recognition—0
A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices—0
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications—0
Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders—0
Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence—0
Detecting Falls with X-Factor Hidden Markov Models—0
Attention-Driven Body Pose Encoding for Human Activity Recognition—0
AI-Powered Non-Contact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing—0
Activity Modeling in Smart Home using High Utility Pattern Mining over Data Streams—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