Traffic Anomaly Prediction System Using Predictive Network

Abstract

Anomaly anticipation in traffic scenarios is one of the primary challenges in action recognition. It is believed that greater accuracy can be obtained by the use of semantic details and motion information along with the input frames. Most state-of-the art models extract semantic details and pre-defined optical flow from RGB frames and combine them using deep neural networks. Many previous models failed to extract motion information from pre-processed optical flow. Our study shows that optical flow provides better detection of objects in video streaming, which is an essential feature in further accident prediction. Additional to this issue, we propose a model that utilizes the recurrent neural network which instantaneously propagates predictive coding errors across layers and time steps. By assessing over time the representations from the pre-trained action recognition model from a given video, the use of pre-processed optical flows as input is redundant. Based on the final predictive score, we show the effectiveness of our proposed model on three different types of anomaly classes as Speeding Vehicle, Vehicle Accident, and Close Merging Vehicle from the state-of-the-art KITTI, D2City and HTA datasets.

Impact of Traffic Flow Rate on the Accuracy of Short-Term Prediction of Origin-Destination Matrix in Urban Transportation Networks

“…Thanks to this, it is possible to, among other things, detect the presence and direction of movement of vehicles, as well as the detection of cyclists, vehicle classification, traffic intensity measurement, and queue measurement. The values of traffic intensities obtained from video remote sensing devices also enable advanced analyses to detect various types of anomalies in traffic volume distributions [1,2].…”

“…However, determining reliable traffic forecasts is difficult due to the complex and dynamic spatial-temporal relationships between traffic flows in different parts of the road network in urban areas [1]. In recent years, much research has been carried out in this field [10,11].…”