Vehicle Detection and Tracking for 511 Traffic Cameras With U-Shaped Dual Attention Inception Neural Networks and Spatial-Temporal Map

Abstract

This paper develops vehicle detection and tracking method for 511 camera networks based on the spatial-temporal map (STMap) as an add-on toolbox for the traveler information system platform. The U-shaped dual attention inception (DAIU) deep-learning model was designed, given the similarities between the STMap vehicle detection task and the medical image segmentation task. The inception backbone takes full advantage of diverse sizes of filters and the flexible residual learning design. The channel attention module augmented the feature extraction for the bottom layer of the UNet. The modified gated attention scheme replaced the skip connection of the original UNet to reduce irrelevant features learned from earlier encoder layers. The designed model was tested on NJ511 traffic cameras for different scenarios covering rainy, snowy, low illumination, and signalized intersections from a key, strategic arterial in New Jersey. The DAIU Net has shown better performance than other mainstream neural networks based on segmentation model evaluation metrics. The proposed scanline vehicle detection was also compared with the state-of-the-art solution for infrastructure-based traffic movement counting solution and demonstrates superior capability. The code for the proposed DAIU model and reference models has been made public with the labeled STMap data to facilitate future research.