In this paper, we address the problem of multi-person detection, tracking and distance estimation in a
complex scenario using multi-cameras. Specifically, we are interested in a vision system for supporting
the driver in avoiding any unwanted collision with the pedestrian.
We propose an approach using Histograms of Oriented Gradients (HOG) to detect pedestrians on
static images and a particle filter as a robust tracking technique to follow targets from frame to frame.
Because the depth map requires expensive computation, we extract depth information of targets using
Direct Linear Transformation (DLT) to reconstruct 3D-coordinates of correspondent points found by
running Speeded Up Robust Features (SURF) on two input images. Using the particle filter the proposed
tracker can efficiently handle target occlusions in a simple background environment. However,
to achieve reliable performance in complex scenarios with frequent target occlusions and complex cluttered
background, results from the detection module are integrated to create feedback and recover the
tracker from tracking failures due to the complexity of the environment and target appearance model
The proposed approach is evaluated on different data sets both in a simple background scenario and a
cluttered background environment. The result shows that, by integrating detector and tracker, a reliable
and stable performance is possible even if occlusion occurs frequently in highly complex environment.
A vision-based collision avoidance system for an intelligent car, as a result, can be achieved.