1. A method comprising: ingesting input data that includes sensor data collected for an area of interest at a traffic intersection; modeling the input data within a computing environment in a plurality of data processing modules executed in conjunction with at least one specifically-configured processor, the data processing modules configured to detect and classify multiple moving objects in the area of interest at the traffic intersection traffic detection zone, by drawing a traffic detection zone comprised of different zones used by the multiple moving objects in the area of interest; analyzing temporal information by examining pixels in the sensor data to associate groups of moving pixels having common pixel characteristics and initially identify a group of moving pixels as a foreground object in the different zones; analyzing spatial information by extracting and examining specific multi-dimensional pixel attributes in the sensor data that include one or more of a pixel histogram, directional edges, and gray scale mean to identify background characteristics and estimate lane structures and other roadway markings within the traffic detection zone, to adaptively learn a background information model of the estimated lane structures and other roadway markings that identify zones for vehicular, cyclist, and pedestrian use among the different zones within the area of interest over time; applying the background information model to determine if one or more of the pixels in the area of interest conform to the background characteristics and estimated lane structures and other roadway markings; and classifying the temporal information and spatial information by determining if a group of moving pixels represent one or more foreground objects inside the zones for vehicular, cyclist, and pedestrian use, and identifying an object type of the one or more foreground objects based on dominant object type features that include one or more of pixel intensity, pixel edges, pixel texture content, pixel shape, object attributes, and object tracking attributes for each object type, wherein the object type is at least one of a bicycle, a truck, a passenger vehicle, a pedestrian, or an incident; and generating, as output data, information representing the object type to a traffic controller system.