Research Article
Pedestrian Detection in Crowded Environments through Bayesian Prediction of Sequential Probability Matrices
Table 1
Features of the image datasets.
| Dataset | Environment | Robot trajectory | Pedestrian behavior |
| (a) ETSII | Urban | Slow, straight | Static or erratic | (b) ITER1 | Rural | Fast, straight | Static | (c) ITER2 | Rural | Fast, erratic | Static | (d) BAHNHOF | Urban | Slow, straight | Parallel to robot | (e) JELMOLI | Urban | Fast, erratic | Several directions | (f) SUNNY DAY | Urban | Fast, straight | Parallel to robot | (g) CAVIAR1 | Indoors | Static | Erratic | (h) CAVIAR2 | Indoors | Static | Static or erratic | (i) CAVIAR3 | Indoors | Static | Static or erratic | (j) CAVIAR4 | Indoors | Static | Erratic, crowded | (k) DAIMLER | Urban | Fast, erratic | Several directions | (l) CALTECH | Urban | Fast, straight | Parallel to robot |
|
|