Wireless Sensor Networks (WSN) provide an economical, convenient alternative solution to existing traffic monitoring systems, such as video recognition and inductive loops. However, there are many technical challenges in on-road real-time traffic information collection with WSN, e.g. accurate vehicle detection in low Signal-Noise-Ratio (SNR) conditions and reliable vehicle speed calculation. In...

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In this paper, a two-step approach for vehicles detection is proposed. The first step of approach is to approximate vehicles’ potential locations through searching for shadow area of vehicle low-part. In order to find these shadows, Haar-like feature with Adaboost was used to train a Haar detector offline and the relearning process with hard training samples is applied to increase detection rat...

Automatic vehicle detection from aerial images is emerging due to the strong demand of large-area traffic monitoring. In this paper, we present a novel framework for automatic vehicle detection from the aerial images. Through superpixel segmentation, we first segment the aerial images into homo-geneous patches, which consist of the basic units during the detection to improve efficiency. By intr...

Traffic data of multiple vehicle types are important for pavement design, traffic operations and traffic control. A new video-based traffic data collection system for multiple vehicle types is developed. By tracking and classifying every passing vehicle under mixed traffic conditions, the type and speed of every passing vehicle are recognised. Finally, the flows and mean speeds of multiple vehi...

The front vehicle detection and alarm system proposed by this article can be applied to forward collision warning (FCW), forward distance estimation (FDE), and time to collision (TTC) estimation. Using the CCD camera to capture the road image in front for the image processing algorism, the image processing algorism is the key point which decides the accuracy and reliability of vehicle safety sy...

The purpose of this study is to successfully train our vehicle detector using R-CNN, Faster R-CNN deep learning methods on a sample vehicle data sets and to optimize the success rate of the trained detector by providing efficient results for vehicle detection by testing the trained vehicle detector on the test data. The working method consists of six main stages. These are respectively; loading...