Real-Time Automatic Traffic Accident Recognition Using HFG

Recently, the problem of automatic traffic accident recognition has appealed to the machine vision community due to its implications on the development of autonomous Intelligent Transportation Systems (ITS). In this paper, a new framework for real-time automated traffic accidents recognition using Histogram of Flow Gradient (HFG) is proposed. This framework performs two major steps. First, HFG-based features are extracted from video shots. Second, logistic regression is employed to develop a model for the probability of occurrence of an accident by fitting data to a logistic curve. In case of occurrence of an accident, the trajectory of vehicle by which the accident was occasioned is determined. Preliminary results on real video sequences confirm the effectiveness and the applicability of the proposed approach, and it can offer delay guarantees for real-time surveillance and monitoring scenarios.

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