Motorcycle detection and classification in urban Scenarios using a model based on Faster R-CNN

This paper has been presented at: 9th International Conference on Pattern Recognition Systems (ICPRS-18)This paper introduces a Deep Learning Convolutional Neutral Network model based on Faster-RCNN for motorcycle detection and classification on urban environments. The model is evaluated in occluded scenarios where more than 60% of the vehicles present a degree of occlusion. For training and evaluation, we introduce a new dataset of 7500 annotated images, captured under real traffic scenes, using a drone mounted camera. Several tests were carried out to design the network, achieving promising results of 75% in average precision (AP), even with the high number of occluded motorbikes, the low angle of capture and the moving camera. The model is also evaluated on low occlusions datasets, reaching results of up to 92% in AP.S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander. The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research. The data and code used for this work is available upon request from the authors

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

Motorcycles are Vulnerable Road Users (VRU) and as such, in addition to bicycles and pedestrians, they are the traffic actors most affected by accidents in urban areas. Automatic video processing for urban surveillance cameras has the potential to effectively detect and track these road users. The present review focuses on algorithms used for detection and tracking of motorcycles, using the surveillance infrastructure provided by CCTV cameras. Given the importance of results achieved by Deep Learning theory in the field of computer vision, the use of such techniques for detection and tracking of motorcycles is also reviewed. The paper ends by describing the performance measures generally used, publicly available datasets (introducing the Urban Motorbike Dataset (UMD) with quantitative evaluation results for different detectors), discussing the challenges ahead and presenting a set of conclusions with proposed future work in this evolving area

Object Detection in Omnidirectional Images

Nowadays, computer vision (CV) is widely used to solve real-world problems, which pose increasingly higher challenges. In this context, the use of omnidirectional video in a growing number of applications, along with the fast development of Deep Learning (DL) algorithms for object detection, drives the need for further research to improve existing methods originally developed for conventional 2D planar images. However, the geometric distortion that common sphere-to-plane projections produce, mostly visible in objects near the poles, in addition to the lack of omnidirectional open-source labeled image datasets has made an accurate spherical image-based object detection algorithm a hard goal to achieve. This work is a contribution to develop datasets and machine learning models particularly suited for omnidirectional images, represented in planar format through the well-known Equirectangular Projection (ERP). To this aim, DL methods are explored to improve the detection of visual objects in omnidirectional images, by considering the inherent distortions of ERP. An experimental study was, firstly, carried out to find out whether the error rate and type of detection errors were related to the characteristics of ERP images. Such study revealed that the error rate of object detection using existing DL models with ERP images, actually, depends on the object spherical location in the image. Then, based on such findings, a new object detection framework is proposed to obtain a uniform error rate across the whole spherical image regions. The results show that the pre and post-processing stages of the implemented framework effectively contribute to reducing the performance dependency on the image region, evaluated by the above-mentioned metric

Real-time Vehicle Detection, Tracking and Counting System Based on YOLOv7

The importance of real-time vehicle detection tracking and counting system based on YOLOv7 is an important tool for monitoring traffic flow on highways. Highway traffic management, planning, and prevention rely heavily on real-time traffic monitoring technologies to avoid frequent traffic snarls, moving violations, and fatal car accidents. These systems rely only on data from timedependent vehicle trajectories used to predict online traffic flow. Three crucial duties include the detection, tracking, and counting of cars on urban roads and highways as well as the calculation of statistical traffic flow statistics (such as determining the real-time vehicles flow and how many different types of vehicles travel). Important phases in these systems include object detection, tracking, categorizing, and counting. The YOLOv7 identification method is presented to address the issues of high missed detection rates of the YOLOv7 algorithm for vehicle detection on urban highways, weak perspective perception of small targets, and insufficient feature extraction. This system aims to provide real-time monitoring of vehicles, enabling insights into traffic patterns and facilitating informed decision-making. In this paper, vehicle detecting, tracking, and counting can be calculated on real-time videos based on modified YOLOv7 with high accuracy