Paper Info
Title
Drone-Based Vehicle Flow Estimation And Its Application To Traffic Conflict Hotspot Detection At Intersections
Abstract
Drones can provide a wider field of view, high mobility and flexibility for monitoring and analyzing traffic flows and safety conditions. In case of a perpendicular viewing angle to the ground, there will be a very less occlusion that can occur and make vehicle tracking be easier. Thus, a dronebased solution will be better for traffic conflict hotspot detection at an interaction. However, due to its observation far from the ground, limited battery time, and bandwidth, this solution should be edge-based and have a good recognition rate in small object detection. However, current edge-based SoTA (state-of-the-art) methods are weak in a small object detection. We propose CoBiF net (Concatenated Bi-Fusion feature pyramid network), a onestage object detection model for a real-time small object detection, which consists of SPP (spatial pyramid pooling), FE (Feature Extractor), CF (Concatenated Feature) block, and BEM (Bottom -up Fusion Module). CoBiF net is memory-and-bandwidth saving for the most edge devices. Extensive experiments on UAI,DT benchmark show the proposed method achieved the SoTA results for the small object detection task in terms of accuracy and efficiency.
Year
DOI
Venue
2020
10.1109/ICIP40778.2020.9190890
2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Keywords
DocType
ISSN
Small object detection, traffic flow estimation, traffic conflict hot spot detection, edge computing
Conference
1522-4880
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Ping-Yang Chen124.12
Jun-Wei Hsieh275167.88
Munkhjargal Gochoo3144.41
Ming-Ching Chang427528.15
Chien-Yao Wang535.14
Yong-Sheng Chen631430.12
h y m liao72353198.72