Traffic Monitoring of Motorcycles during Special Events Using Video Detection

Abstract

2Because of a recent federal initiative, states are now required (as of June 2008) to collect and 3 submit motorcycle VMT data to the FHWA. These data are needed to obtain better counts of 4 motorcycles to evaluate their impact on crashes and traffic flow. However, there is concern 5 about the quality of data submitted. Many states have identified problems with using automatic 6 traffic recorders to account for motorcycle traffic. Existing sensors exhibit difficulties in 7 counting motorcycles that travel side by side or close behind each other, they have difficulty in 8 distinguishing larger motorcycles from passenger vehicles, and magnetic counters in particular 9 do not sense motorcycles that do not pass over or travel close enough to the sensor.

Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

“…Such threshold can be dynamic or constant, global or local. This approach is used by Kanhere et al [36] for motorcycle detection, starting with a manual "six clicks" camera calibration, defining three lines parallel to the image projection, then applying background subtraction to identify blobs of moving objects. Finally, features are identified using the estimated height and the local slope (in real world coordinates) projected from foreground blobs and grouped by region growth.…”

Detection of Ship Using Image Processing and Neural Network

“…From these recordings, ships detection is performed, notably those exercising illegal fishing. As technologies develop nowadays, digital image processing techniques and artificial intelligence can be used for analysis of the object on digital image such as for motorcycle detection [2][3][4], renal tumor detection [5], traffic monitoring [6], virtual reality robot [7], motorcyclist's helmet wearing detection [8], blood typing [9], cotton pests detection [10], rice variety identification [11] and analysis of road damage [12]. Meanwhile, the studies on ship detection have been complied by percolating several methods, such as Support Vector Machine (SVM) [13][14], visual saliency and histogram [15], Discrete Wavelet Transform (DWT) [16], combination statistical methods, mathematical morphology and other signalprocessing techniques [17], and sea surface analysis [18].…”

Motorcycle Classification in Urban Scenarios using Convolutional Neural Networks for Feature Extraction

“…Until now most motorcycle video detection systems are implemented using conventional feature extraction techniques such as 3D models [1] [2], vehicle dimensions [3] [4] [5], symmetry, color, shadow, geometrical features and texture and even wheel contours [6]. Other works report the use of stable features [7], HOG for evaluation of helmet presence [8] [9], variations of HOG [10] [11], and the use of SIFT, DSIFT and SURF [12].…”