Iteris, Inc.

Dilip Swaminathan

Michael T Whiting

Yan Gao

Todd William Kreter

Shashank Jayaram Shivakumar

Robert J Hwang

Manu P Jain

Wing Lam

Robert Y Ung

A framework for precision traffic analysis combines traffic sensor data from multiple sensor types, combining the strengths of each sensor type within various conditions in an intersection or roadway in which traffic activity occurs. The framework calibrates coordinate systems in images taken of the same area by multiple sensors, so that fields of view from one sensor system are transposed onto fields of view from other sensor systems to fuse the images taken into a combined detection zone, and so that objects are properly detected and classified for enhanced traffic signal control.

Enhanced traffic detection by fusing multiple sensor data

Pedestrian detection and counting for traffic intersection control analyzes characteristics of a field of view of a traffic detection zone to determine a location and size of a pedestrian area, and applies protocols for evaluating pixel content in the field of view to identify individual pedestrians. The location and size of a pedestrian area is determined based either on locations of vehicle and bicycle detection areas or on movement of various objects within the field of view. Automatic pedestrian speed calibration with a region of interest for pedestrian detection is accomplished using lane and other intersection markings in the field of view. Detection and counting further includes identifying a presence, volume, velocity and trajectory of pedestrians in the pedestrian area of the traffic detection zone.

Pedestrian tracking at a traffic intersection to identify vulnerable roadway users for traffic signal timing, pedestrian safety, and traffic intersection control

Calibration of pedestrian speed with detection zone for traffic intersection control

Pedestrian counting and detection at a traffic intersection based on object movement within a field of view

Pedestrian counting and detection at a traffic intersection based on location of vehicle zones

A multi-object zonal traffic detection framework analyzes temporal and spatial information from one or more sensors in a classification engine that identifies and differentiates objects within a single identified traffic detection zone. The classification engine applies a whole scene analysis and an adaptive background zone detection model to classify cars, trucks, bicycles, pedestrians, incidents, and other objects within the single identified traffic detection zone and generates counts for each object type for traffic system management.

Traffic detection with multiple outputs depending on type of object detected

A traffic management apparatus and system performs data processing functions on images in a video data stream to analyze differences between portions of the images and account for movement of a camera at a traffic intersection or other such environment. The traffic management apparatus and system is configured to be placed on a span wire or other non-fixed position at or near the traffic intersection.

Dynamic zone stabilization and motion compensation in a traffic management apparatus and system

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Costa Mesa, CA, United States of America

Dilip Swaminathan