A Review of Computer Vision Techniques for the Analysis of Urban Traffic

By Norbert Erich Buch, Sergio A. Velastin, James Orwell. IEEE transactions on intelligent transportation systems (Print), 2011

681 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/af858b8f788c81d40c3c21568d3cd0d058dcc9f7

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In Computer Vision and Pattern Recognition

In Safety, Risk, Reliability and Quality

Citations in the corpus

A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit (2021, background): “Later on, advancements in object detection techniques led to their increasing adoption in areas that included the manufacturing industry with optical inspections [13], video surveillance [14], forensics [15,16], medical image analysis [17–19], autonomous vehicles [20], and trafﬁc monitoring [21].”
Computer vision in automated parking systems: Design, implementation and challenges (2017, background): “Vehicle detection and tracking is often done in the context of front camera detection [19] for applications like auto emergency braking or in traffic surveillance applications [20].”
Survey on Deep Neural Networks in Speech and Vision Systems (2019, background): “However, recent advancements in IS are permeating almost every aspect of our life with the introduction of intelligent transportation [1-10], intelligent diagnosis and health monitoring for precision medicine [11-14], robotics and automation in home appliances [15], virtual online assistance [16], e”
Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics (2020, methodology): “We began with a representative list of differencing features used by the CV community [21], and grouped them in terms of the amount of computation required for extraction.”
Semantic Foggy Scene Understanding with Synthetic Data (2017, neutral): “Cameras and the accompanying vision algorithms are widely used for applications such as surveillance [9],”
Depth-Attentional Features for Single-Image Rain Removal (2019, background): “, object detection [17] and tracking [40] for surveillance [2], autonomous driving [20], and driver assistance [33].”
Rain Removal in Traffic Surveillance: Does it Matter? (2018, background): “Bad weather, including rain and snow, is generally acknowledged as a challenge in computer vision [7], but little work has been undertaken to identify the severity of this problem.”
Short-term traffic forecasting: Where we are and where we’re going (2014, background): “Departing for the classical loop detector data collection that is well-documented and researched, there are currently a variety of sources to collect traffic data such as video based technologies (Buch et al., 2011).”