Detection of helmets on motorcyclists

IEEE Trans. Intell. Transport. Syst.

Branch

2021

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.

Section: A Methods Based On Appearancementioning

confidence: 99%

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

Espinosa

IEEE Trans. Intell. Transport. Syst.

Branch

2021

“…Feature descriptors such as Histogram of oriented gradients (HOG), Scale-invariant feature transform (SIFT), and Local binary patterns (LBP) are compared in [14] and [15] for motorcycle detection. For helmet detection in motorcycles riders Speeded up robust features (SURF), Haar-like features (HAAR) and HOG [16] have been used as feature descriptors. Meanwhile, in [17], they use hybrid descriptor based on colour for helmet identification.…”

Section: Motorcycle Detectionmentioning

confidence: 99%

“…Neural networks (NN) such as the Multilayer Perceptron (MLP) have been proposed for motorcycle detection and classification, even though their architectures require tuning of many parameters and the implemented loss function may not converge to a local optimum. Nevertheless, NN are used for helmet detection in [16,31]. There is also Fuzzy neural network (FNN) [24], but without a significant number of motorcycles to detect in their dataset.…”

Section: Motorcycle Detectionmentioning

confidence: 99%

EspiNet V2: a region based deep learning model for detecting motorcycles in urban scenarios

Oviedo

Bedoya

2019

DYNA

This paper presents “EspiNet V2” a Deep Learning model, based on the region-based detector Faster R-CNN. The model is used for the detection of motorcycles in urban environments, where occlusion is likely. For training, two datasets are used: the Urban Motorbike Dataset (UMD-10K) of 10,000 annotated images, and the new SMMD (Secretaría de Movilidad Motorbike Dataset), of 5,000 images captured from the Traffic Control CCTV System in Medellín (Colombia). Results achieved on the UMD-10K dataset reach 88.8% in average precision (AP) even when 60% motorcycles were occluded, and the images were captured from a low angle and a moving camera. Meanwhile, an AP of 79.5% is reached for SSMD. EspiNet V2 outperforms popular models such as YOLO V3 and Faster R-CNN (VGG16 based) trained end-to-end for those datasets

“…Based on object proposal algorithms, two stage CNN models integrate region proposal and classification in a single architecture, such as Fast R-CNN [29] and faster R-CNN [30] based models for vehicle detection and classification [31][32] [33]. Motivated by safety measures, helmet detection in motorcycle riders has inspired research using geometrical features [34], hand crafted features (HOG, SIFT, LBP, CHT [35] [36] [12]), neuro-fuzzy detectors [37] and neural networks [38]. Nevertheless, there are few reports exploring CNNs for motorcycle classification, e.g.…”

Section: Introductionmentioning

confidence: 99%

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

Espinosa

9th International Conference on Pattern Recognition Systems (ICPRS 2018)

Branch

2018

This paper introduces a Deep Learning Convolutional NeutralNetwork 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.