Inner-ellular automata method for video pedestrian state forecasting

Abstract:

Forecasting pedestrian states with the help of video analysis is a significant mean to prevent accidents in public places. An improved Inner-grid Parking Cellular Automata (IPCA) model is proposed to forecast pedestrian state. Motion tracking and scenario modeling are firstly achieved by optical flow method, and then motion states of pedestrian are used to adjust parking time in cellular adaptively to improve forecasting accuracy. Here, the state of an incoming video frame is acquired by analyzing the interaction between micelles, which is provided by the forecasting result of IPCA. After that, a feedback algorithm is employed to revise the forecasting result so that the model could reflect precisely the change of pedestrian state in the scenario. A criterion is also proposed to judge the abnormal state of a video frame, which is collision between two micelles. Compared to other traditional detection methods, IPCA based model has a good ability in predicting the abnormal state ahead of time and obtaining better accuracy.