Tracking the Unseen: Autonomous Multi-Object Motion Analysis in Complex Environments

In typical object detection and tracking methods, separating the image background clutter from the object of interest is a challenging but necessary task. The more dynamic the background clutter, the more difficult the desired object is to trace. In this paper, instead of separating the unwanted sections of an image from the object, we decided to use the dynamic background clutter to help detect the presence of an object and to track its periodic motion. With the use of digital signal processing in combination with partial fast Fourier transformation (pFFT), an algorithmic process called Periodic Motion Detection (PMD) is used to autonomously detect an object by plotting the fluctuations that occur within a background when an object comes into the field-of-view (FOV). These changes are then converted into a digital signal format and fed into a pFFT to determine the frequency in which the object appears. The detection of an object becomes apparent when a pattern emerges in the random background fluctuation signal and when the level of change within that signal reaches a specific threshold. Experimental results show that photometric disturbances, which is a type of dynamic background clutter, can be used to autonomously detect multiple objects and predict the frequency of the appearances of those objects.