Behavior interpretation from traffic video streams

Copyright © 2003 IEEEThis paper considers video surveillance research applied to traffic video streams. We present a framework for analyzing and recognizing different possible behaviors from image sequences acquired from a fixed camera. Two types of interactions have been mainly considered. In one there is interaction between two or more mobile objects in the field of view (FOV) of the camera. The other is interaction between a mobile object and static objects in the environment. The framework is based on two types of a priori knowledge: (1) the contextual knowledge of the camera's FOV, in terms of the description of the different static objects of the scene and (2) sets of predefined behaviors which need to be analyzed in different contexts. At present the system is designed to recognize behavior from stored videos and retrieve the frames in which the specific behaviors took place. We demonstrate successful behavior recognition results for pedestrian-vehicle interaction and vehicle-checkpost interactions

Video analytics on the MLK Smart Corridor testbed

With the predicted boom of urban environment populations in the next 30 years, many new challenges in urban transportation will surface. In an effort to mitigate these, the Center for Urban Informatics and Progress (CUIP) has been introduced along with its testbed. One opportunity this testbed provides is the ability to utilize computer vision and video analytics to anonymously gather data on how citizens traverse the city. This thesis shall discuss an approach to real-time object tracking that serves as a basis for further analytics such as traffic flow data collection and near-miss detection. The proposed video analytics platform will aid citizens with their day-to-day commute through the corridor by deriving real-time data based on actual behavior seen in the citizens\u27 commute. Furthermore, since the testbed is ever-expanding in both hardware and size the algorithms and software proposed in this thesis are designed to prioritize scalability

Street Viewer: An Autonomous Vision Based Traffic Tracking System

The development of intelligent transportation systems requires the availability of both accurate traffic information in real time and a cost-effective solution. In this paper, we describe Street Viewer, a system capable of analyzing the traffic behavior in different scenarios from images taken with an off-the-shelf optical camera. Street Viewer operates in real time on embedded hardware architectures with limited computational resources. The system features a pipelined architecture that, on one side, allows one to exploit multi-threading intensively and, on the other side, allows one to improve the overall accuracy and robustness of the system, since each layer is aimed at refining for the following layers the information it receives as input. Another relevant feature of our approach is that it is self-adaptive. During an initial setup, the application runs in learning mode to build a model of the flow patterns in the observed area. Once the model is stable, the system switches to the on-line mode where the flow model is used to count vehicles traveling on each lane and to produce a traffic information summary. If changes in the flow model are detected, the system switches back autonomously to the learning mode. The accuracy and the robustness of the system are analyzed in the paper through experimental results obtained on several different scenarios and running the system for long periods of time