Table 3 Comparison with other research work
From: Motion detection using binocular image flow in dynamic scenes
Category | Approach | Object type under detection | Precision (%) | Recall (%) | Note |
|---|---|---|---|---|---|
Self-adaptive background matching | BBM-based Cauchy distribution [4] | Pedestrian | 98.8 | 88.1 | Video surveillance with static camera |
Vehicle | 91.3 | 72.0 | Â | ||
Optical flow | Hidden Markov model (HMM) [12] | Vehicle only | – | 86.6 |  |
Stereo-motion fusion | Longuet-Higgins-Equations combined with extended Kalman filter [17] | Pedestrian or car | – | 96 | Result for feature points detection. The recall definition is slightly different from ours |
|  | Cuboidal object model with extended Kalman filter [18] | Pedestrian or car | – | 71.3 | Result for object tracking |
| Â | Our approach | Pedestrian | 94.0 | 92.2 | Â |
Vehicle | 94.5 | 93.1 | Â |