Vehicle accidents are one of the major causes of deaths in South African un-
derground mines. A computer vision-based pedestrian detection and track-
ing system is presented in this research that will assist locomotive drivers
in operating their vehicles safer. The detection and tracking system uses a
combination of thermal and three-dimensional (3D) imagery for the detec-
tion and tracking of people. The developed system uses a segment-classify-
track methodology which eliminates computationally expensive multi-scale
classi cation. A minimum error thresholding algorithm for segmentation is
shown to be e ective in a wide range of environments with temperature up to
26 C and in a 1000 m deep mine. The classi er uses a principle component
analysis and support vector classi er to achieve a 95% accuracy and 97%
speci city in classifying the segmented images. It is shown that each detec-
tion is not independent of the previous but the probability of missing two
detections in a row is 0.6%, which is considered acceptably low. The tracker
uses the Kinect's structured-light 3D sensor for tracking the identi ed peo-
ple. It is shown that the useful range of the Kinect is insu cient to provide
timeous warning of a collision. The error in the Kinect depth, measurements
increases quadratically with depth resulting in very noisy velocity estimates
at longer ranges. The use of the Kinect for the tracker demonstrates the
principle of the tracker but due to budgetary constraints the replacement of
the Kinect with a long range sensor remains future work
