Object Detection and Motion Analysis in a Low Resolution 3-D Model

With augmenting security concerns and decreasing costs of surveillance and computing equipment, research on automated systems for object detection has been increasing, but the majority of the studies focus their attention on sequences where high-resolution objects are of interest. The main objective of the work reported here is the detection and extraction of information of low-resolution objects (e.g., objects that are so small or so far away from the camera that they occupy only tens of pixels) in order to provide a base for higher level information operations such as classification and behavioral analysis. The system proposed is composed of four stages (preprocessing, background modeling, information extraction, and post processing) and uses context-based region-of-importance selection, histogram equalization, background subtraction, biological motion analysis, and morphological filtering techniques. The result is a system capable of detecting and tracking low -resolution objects in a controlled background scene which can be a base for systems with higher complexity.