Vehicle flow estimation has many potential smart cities and transportation applications. Many cities have existing camera networks which broadcast image feeds; however, the resolution and frame-rate are too low for existing computer vision algorithms to accurately estimate flow. In this work, we present a computer vision and deep learning framework for vehicle tracking. We demonstrate a novel tracking pipeline which enables accurate flow estimates in a range of environments under low resolution and frame-rate constraints. We demonstrate that our system is able to track vehicles in New York City's traffic camera video feeds at 1 Hz or lower frame-rate, and produces higher traffic flow accuracy than popular open source tracking frameworks.
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This content will become publicly available on March 13, 2025
A Comprehensive Analysis of Object Detectors in Adverse Weather Conditions
In this paper, we meticulously examine the robustness of computer vision object detection frameworks within the intricate realm of real-world traffic scenarios, with a particular emphasis on challenging adverse weather conditions. Conventional evaluation methods often prove inadequate in addressing the complexities inherent in dynamic traffic environments—an increasingly vital consideration as global advancements in autonomous vehicle technologies persist. Our investigation delves specifically into the nuanced performance of these algorithms amidst adverse weather conditions like fog, rain, snow, sun flare, and more, acknowledging the substantial impact of weather dynamics on their precision. Significantly, we seek to underscore that an object detection framework excelling in clear weather may encounter significant challenges in adverse conditions. Our study incorporates in-depth ablation studies on dual modality architectures, exploring a range of applications including traffic monitoring, vehicle tracking, and object tracking. The ultimate goal is to elevate the safety and efficiency of transportation systems, recognizing the pivotal role of robust computer vision systems in shaping the trajectory of future autonomous and intelligent transportation technologies.
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- Award ID(s):
- 2025234
- NSF-PAR ID:
- 10521809
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-6929-8
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Location:
- Princeton, NJ, USA
- Sponsoring Org:
- National Science Foundation
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