Weigh-in-Motion Sensors and Traffic Monitoring Systems. State of the Art and Perspectives

Abstract

Weigh-in-motion (WIM) sensors allow the control of vehicle weights without disruption of traffic. By monitoring traffic and by reducing the number of overweight vehicles, the WIM sensors bring very important savings. This paper discusses the present status and developmental trends of weigh-in-motion (WIM) technologies. Both commercial and new types of WIM sensors are presented. Strengths and weaknesses of different type of WIM sensors are discussed. It is also presented the tendency to equip the WIM systems with different types of sensors, in order to evaluate other effects: reducing the fuel consumption, emission of pollutants, noise and vibrations, etc. Possible trends for the further development of WIM sensors are anticipated.

People Flow Monitoring Based on Deep Learning

“…Target detection algorithms can be divided into feature extraction-based algorithms [1] and convolutional neural network-based target detection [2]. The target detection algorithm uses feature extraction operators such as SITF [3], LBP [4], HOG [5], or Haar [6] to extract features from target candidate regions and classifiers such as SVM to detect and classify targets. Felzenszwalb et al [7] combined HOG with SVM to propose a deformable part model DPM, which stands out among object detection algorithms.…”