The traffic congestion hits most big cities in the world - threatening long delays and serious reductions in air quality. City and local government officials continue to face challenges in optimizing crowd flow, synchronizing traffic and mitigating threats or dangerous situations. One of the major challenges faced by city planners and traffic engineers is developing a robust traffic controller that eliminates traffic congestion and imbalanced traffic flow at intersections. Ensuring that traffic moves smoothly and minimizing the waiting time in intersections requires automated vehicle detection techniques for controlling the traffic light automatically, which are still challenging problems. In this paper, we propose an intelligent traffic pattern collection and analysis model, named TPCAM, based on traffic cameras to help in smooth vehicular movement on junctions and set to reduce the traffic congestion. Our traffic detection and pattern analysis model aims at detecting and calculating the traffic flux of vehicles and pedestrians at intersections in real-time. Our system can utilize one camera to capture all the traffic flows in one intersection instead of multiple cameras, which will reduce the infrastructure requirement and potential for easy deployment. We propose a new deep learning model based on YOLOv2 and adapt the model for the traffic detection scenarios. To reduce the network burdens and eliminate the deployment of network backbone at the intersections, we propose to process the traffic video data at the network edge without transmitting the big data back to the cloud. To improve the processing frame rate at the edge, we further propose deep object tracking algorithm leveraging adaptive multi-modal models and make it robust to object occlusions and varying lighting conditions. Based on the deep learning based detection and tracking, we can achieve pseudo-30FPS via adaptive key frame selection.
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Towards Efficient Traffic Monitoring for Science DMZ with Side-Channel based Traffic Winnowing
As data-intensive science becomes the norm in many fields of science, high-performance data transfer is rapidly becoming a core scientific infrastructure requirement. To meet such a requirement, there has been a rapid growth across university campus to deploy Science DMZs. However, it is challenging to efficiently monitor the traffic in Science DMZ because traditional intrusion detection systems (IDSes) are equipped with deep packet inspection (DPI), which is resource-consuming. We propose to develop a lightweight side-channel based anomaly detection system for traffic winnowing to reduce the volume of traffic finally monitored by the IDS. We evaluate our approach based on the experiments in a Science DMZ environment. Our evaluation demonstrates that our approach can significantly reduce the resource usage in traffic monitoring for Science DMZ.
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- NSF-PAR ID:
- 10072682
- Date Published:
- Journal Name:
- ACM International Workshop on Security in Software Defined Networks and Network Function Virtualization
- Page Range / eLocation ID:
- 55 to 58
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3180465.3180474
