Human Activity Recognition
Classify various human activities
Papers
Showing 51–60 of 744 papers
All datasetsRHMOAD datasetPAMAP2HARHMDB51MM-FitRadar Dataset (DIAT-μRadHAR: Radar micro-Doppler Signature dataset for Human Suspicious Activity Recognition)UCF-101
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Dual-Stream C3D | Accuracy (Top-1) | 71.06 | — | Unverified |
| 2 | C3D | Accuracy (Top-1) | 70.3 | — | Unverified |
| 3 | Dual-Stream ConvNet | Accuracy (Top-1) | 62.77 | — | Unverified |
| 4 | SlowFast (101) | Accuracy (Top-1) | 45.28 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | ESTIE + VGG16 (transfer-learning) | Accuracy | 95.22 | — | Unverified |
| 2 | STIE + VGG16 (transfer-learning) | Accuracy | 94.77 | — | Unverified |
| 3 | STIE + VGG16(fine-tuning) | Accuracy | 86.81 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | AFVF | Accuracy | 0.97 | — | Unverified |
| 2 | Selective HAR Clustering | NMI | 0.88 | — | Unverified |
| 3 | Unsupervised embedding learning for human activity recognition using wearable sensor data | NMI | 0.87 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | LMSS | Accuracy | 1 | — | Unverified |
| 2 | AFVF | Accuracy | 0.99 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Label-Ranker | Accuracy | 61.18 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | unsupervised statistical feature guided diffusion model | F1 - macro | 0.44 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | DIAT-RadHARNet | 1:1 Accuracy | 99.22 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Label-Ranker | Accuracy | 89.5 | — | Unverified |