ISSN : 1796-203X
Volume : 2    Issue : 6    Date : August 2007

Integrated Detection, Tracking and Recognition for IR Video-based Vehicle Classification
Xue Mei, Shaohua Kevin Zhou, Hao Wu, and Fatih Porikli
Page(s): 1-8
Full Text:
PDF (691 KB)

We present an approach for vehicle classification in IR video sequences by integrating detection,
tracking and recognition. The method has two steps. First, the moving target is automatically
detected using a detection algorithm. Next, we perform simultaneous tracking and recognition using
an appearance-model based particle filter. We present a probabilistic algorithm for tracking and
recognition that incorporates robust template matching and incremental subspace update. There
are two template matching methods used in the tracker: one is robust to small perturbation and the
other to background clutter. Each method yields a probability of matching. The templates are
represented using mixed probabilities and updated when the appearance models cannot
adequately represent the variations in object appearance. We also model the tracking history using
a nonlinear subspace described by probabilistic kernel principal components analysis, which
provides a third probability. The most-recent tracking result is incrementally integrated into the
nonlinear subspace by augmenting the kernel Gram matrix with one row and one column. The
product of the three probabilities is defined as the observation likelihood used in a particle filter to
derive the tracking and recognition result. The tracking result is evaluated at each frame. Low
confidence in tracking performance initiates a new cycle of detection, tracking and classification. We
demonstrate the robustness of the proposed method using outdoor IR video sequences.

Index Terms
detection, tracking, recognition, tracking evaluation, IR video