TY - GEN
T1 - Real-time automatic traffic accident recognition using HFG
AU - Sadek, Samy
AU - Al-Hamadi, Ayoub
AU - Michaelis, Bernd
AU - Sayed, Usama
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Accident recognition
KW - Logistic model
KW - Optical flow
UR - http://www.scopus.com/inward/record.url?scp=78149482366&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2010.817
DO - 10.1109/ICPR.2010.817
M3 - Conference contribution
AN - SCOPUS:78149482366
SN - 9780769541099
T3 - Proceedings - International Conference on Pattern Recognition
SP - 3348
EP - 3351
BT - Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
T2 - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Y2 - 23 August 2010 through 26 August 2010
ER -
