This paper addresses two problems commonly associated with video target tracking system. First, video target
detection and tracking usually require extensive searching in a large space to find the best matches for preregistered
templates. Existing fast search methods cannot guarantee a global optimal match, which results in
substandard performance. To obtain a true global match, a full search at the pixel or sub-pixel level is required.
Obviously, this introduces significant computational overhead, which limits the implementation of these algorithms
in real-time applications. In this paper, we propose a fast method to compute two-dimensional normalized
cross-correlations to efficiently find the global optimal match result from a large image area. Comparisons and
complexity analysis are provided to show the efficiency of the proposed algorithm. Second, another challenge
commonly faced by detection and tracking systems is the accurate detection of target orientation in a twodimensional
image. This problem is motivated by applications where the walk-in and walk-out people need to
be detected and a fast image registration method is needed to compensate the change in rotation, translation
and size, which is natural since the target's distance from the camera is changing dramatically. To address this
issue, we propose a novel and efficient eigenvector-based method to detect target orientation and apply it into
automatic human recognition system. Experimental and real-world test results verify that the proposed fast
algorithm achieves similar accuracy as the recursive registration method which is computationally expensive.