1 research outputs found
Intelligent Intersection: Two-Stream Convolutional Networks for Real-time Near Accident Detection in Traffic Video
In Intelligent Transportation System, real-time systems that monitor and
analyze road users become increasingly critical as we march toward the smart
city era. Vision-based frameworks for Object Detection, Multiple Object
Tracking, and Traffic Near Accident Detection are important applications of
Intelligent Transportation System, particularly in video surveillance and etc.
Although deep neural networks have recently achieved great success in many
computer vision tasks, a uniformed framework for all the three tasks is still
challenging where the challenges multiply from demand for real-time
performance, complex urban setting, highly dynamic traffic event, and many
traffic movements. In this paper, we propose a two-stream Convolutional Network
architecture that performs real-time detection, tracking, and near accident
detection of road users in traffic video data. The two-stream model consists of
a spatial stream network for Object Detection and a temporal stream network to
leverage motion features for Multiple Object Tracking. We detect near accidents
by incorporating appearance features and motion features from two-stream
networks. Using aerial videos, we propose a Traffic Near Accident Dataset
(TNAD) covering various types of traffic interactions that is suitable for
vision-based traffic analysis tasks. Our experiments demonstrate the advantage
of our framework with an overall competitive qualitative and quantitative
performance at high frame rates on the TNAD dataset.Comment: Submitted to ACM Transactions on Spatial Algorithms and Systems
(TSAS); Special issue on Urban Mobility: Algorithms and Systems. arXiv admin
note: text overlap with arXiv:1703.07402 by other author