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 526–550 of 1322 papers

TitleStatusHype
Predicting User-specific Future Activities using LSTM-based Multi-label Classification—0
Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity?—0
Discriminating sensor activation in activity recognition within multi-occupancy environments based on nearby interaction—0
Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Data—0
Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones—0
The Contribution of Human Body Capacitance/Body-Area Electric Field To Individual and Collaborative Activity RecognitionCode0
IMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and TextCode1
Performance of different machine learning methods on activity recognition and pose estimation datasets—0
A Symbolic Representation of Human Posture for Interpretable Learning and Reasoning—0
MMTSA: Multimodal Temporal Segment Attention Network for Efficient Human Activity RecognitionCode0
Temporal Feature Alignment in Contrastive Self-Supervised Learning for Human Activity RecognitionCode1
Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition—0
Smart-Badge: A wearable badge with multi-modal sensors for kitchen activity recognition—0
Robust Trajectory-based Density Estimation for Geometric Structure Recovery: Theory and Applications—0
RALACs: Action Recognition in Autonomous Vehicles using Interaction Encoding and Optical FlowCode0
Low-Resolution Action Recognition for Tiny Actions Challenge—0
DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness—0
Heterogeneous Recurrent Spiking Neural Network for Spatio-Temporal Classification—0
Lightweight Transformers for Human Activity Recognition on Mobile DevicesCode1
Contrastive Learning for Time Series on Dynamic Graphs—0
An Overview of Violence Detection Techniques: Current Challenges and Future Directions—0
Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition—0
Out-of-Distribution Representation Learning for Time Series Classification—0
TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation—0
BON: An extended public domain dataset for 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