I-Corps: Real-Time Traffic Congestion Detection from Surveillance Videos

University Of Delaware, Newark DE

Investigators

Abstract

This I-Corps team proposes the development of a real-time traffic congestion detection system from surveillance videos. The proposed technology for detecting traffic congestion involves two major components: (a) vehicle detection and tracking; and (b) event classification. The research team will use computer vision techniques for detecting vehicles in single frames. By analyzing consecutive frames, vehicle speed and relative locations will be estimated. For the classification of events, the team will use their expertise in graph kernels and machine learning. A sequence of consecutive frames can be used to create a graph, where the nodes represent vehicles labeled with local features, such as speed and location. Neighboring nodes in the graph are connected by edges labeled with the distance between their respective vehicles. A fast kernel function for graphs will be developed and used by a binary classifier which will be trained for the task of recognizing traffic congestion. As an extension, a multi-class classifier can also be trained to distinguish different types of traffic events, such as, high, moderate, or low traffic congestion, accident, or normal traffic. The proposed product is a great opportunity for transforming state-of-the-art computational techniques into new technologies that can directly have societal and commercial impacts. A real time traffic congestion detection system has as their primary targets government agencies and departments across the nation. With a real time detector of congestion, one will be able to track simultaneously hundreds or thousands of cameras at the same time, discovering incidents that can enhance traffic management. Decision makers can use the proposed product for dynamic traffic assignment, incident discovery, and improved management of evacuation systems. Society may be impacted as a whole with savings in commuting time and delay costs. This product can also be used for developing web applications or apps for mobile devices, bringing real time traffic information to motorists.