Pedestrian detection is one of the most important components in
driverassistance systems. In this paper, we propose a monocular vision system
for real-time pedestrian detection and tracking during nighttime driving with
a near-infrared (NIR) camera.
Three modules (region-of-interest (ROI) generation, object classification, and
tracking) are integrated in a cascade, and each utilizes complementary visual
features to distinguish the objects from the cluttered background in the range of
20-80 m. Based on the common fact that the objects appear brighter than the
nearby background in nighttime NIR images, efficient ROI generation is done
based on the dual-threshold segmentation algorithm.
As there is large intraclass variability in the pedestrian class, a
treestructured, two-stage detector is proposed to tackle the problem
through training separate classifiers on disjoint subsets of different image
sizes and arranging the classifiers based on Haar-like and histogram-of orientedgradients
(HOG) features in a coarse-to-fine manner.
To suppress the false alarms and fill the detection gaps, template-matchingbased
tracking is adopted, and multiframe validation is used to obtain the final
results. Results from extensive tests on both urban and suburban videos
indicate that the algorithm can produce a detection rate of more than 90% at
the cost of about 10 false alarms/h and perform as fast as the frame rate (30
frames/s) on a Pentium IV 3.0-GHz personal computer, which also
demonstrates that the proposed system is feasible for practical applications
and enjoys the advantage of low implementation cost.
