Vehicle Detection and Tracking Based on GMM and Enhanced Camshift Algorithm

Zhong, Kaiyang; Zhang, Zhaoyang; Zhao, Zhengyu

doi:10.11648/j.jeee.20180602.11

Cited by 4 publications

(1 citation statement)

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“…Subtracting the current image from a background image transforms an original image into a silhouette with blobs representing vehicles. A Gaussian mixture model (GMM) has also been widely used to determine whether pixels correspond to background or foreground in a probabilistic manner [24], [25]. An optical flow method is another robust method used to detect and track moving vehicles [26].…”

Section: Related Workmentioning

confidence: 99%

Image-Based Learning to Measure the Stopped Delay in an Approach of a Signalized Intersection

2019

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Traffic delays are inevitable when evaluating the performance of a signalized intersection, but these delays cannot be directly measured in the field based on existing spot detectors. Traffic-light controllers have adopted a reinforcement learning (RL) algorithm, which is currently prevalent in the field of study and requires real-time measurement of traffic delays to derive the state and reward for each time period. No RL-based study, however, has provided a robust way to measure traffic delays. In order to bridge the gap, we devised a convolutional neural network (CNN) to directly measure traffic delays from video footage in an end-to-end manner. The proposed methodology proved superior to both a state-of-the-art vision technology and an analytic formula that has widely been used to estimate delays. Furthermore, a robust method to secure labeled data without human input was suggested based on a cycle-consistent adversarial network (CycleGAN). INDEX TERMSTraffic delay estimation, image-based learning, deep convolutional neural network.

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Section: Related Workmentioning

confidence: 99%