  In this paper, we introduce an innovative approach for extracting
trajectories from a camera sensor in GPS-denied environments, leveraging visual
odometry. The system takes video footage captured by a forward-facing camera
mounted on a vehicle as input, with the output being a chain code representing
the camera's trajectory. The proposed methodology involves several key steps.
Firstly, we employ phase correlation between consecutive frames of the video to
extract essential information. Subsequently, we introduce a novel chain code
method termed "dynamic chain code," which is based on the x-shift values
derived from the phase correlation. The third step involves determining
directional changes (forward, left, right) by establishing thresholds and
extracting the corresponding chain code. This extracted code is then stored in
a buffer for further processing. Notably, our system outperforms traditional
methods reliant on spatial features, exhibiting greater speed and robustness in
noisy environments. Importantly, our approach operates without external camera
calibration information. Moreover, by incorporating visual odometry, our system
enhances its accuracy in estimating camera motion, providing a more
comprehensive understanding of trajectory dynamics. Finally, the system
culminates in the visualization of the normalized camera motion trajectory.

Computer Vision and Pattern Recognition;Graphics;Multimedia;Robotics