Title: Robust Multi-view Pedestrian Tracking Using Neural Networks,
Abstract: In this paper, we present a real-time robust multi-view pedestrian detection
and tracking system for video surveillance using neural networks which can be
used in dynamic environments. The proposed system consists of two phases:
multi-view pedestrian detection and tracking. First, pedestrian detection
utilizes background subtraction to segment the foreground blob. An adaptive
background subtraction method where each of the pixel of input image models as
a mixture of Gaussians and uses an on-line approximation to update the model
applies to extract the foreground region. The Gaussian distributions are then
evaluated to determine which are most likely to result from a background
process. This method produces a steady, real-time tracker in outdoor
environment that consistently deals with changes of lighting condition, and
long-term scene change. Second, the Tracking is performed at two phases:
pedestrian classification and tracking the individual subject. A sliding window
is applied on foreground binary image to select an input window which is used
for selecting the input image patches from actually input frame. The neural
networks is used for classification with PHOG features. Finally, a Kalman
filter is applied to calculate the subsequent step for tracking that aims at
finding the exact position of pedestrians in an input image. The experimental
result shows that the proposed approach yields promising performance on
multi-view pedestrian detection and tracking on different benchmark datasets.