Transforming traffic surveillance: a YOLO-based approach to detecting helmetless riders through CCTV

CCTV systems, while ubiquitous for traffic surveillance in Indonesian roadways, remain underutilized in their potential. The integration of AI and Computer Vision technologies can transform CCTV into a valuable tool for law enforcement, specifically in monitoring and addressing helmet non-compliance among motorcycle riders. This study aims to develop an intelligent system for the accurate detection of helmetless motorcyclists using image analysis. The approach relies on deep learning, involving the creation of a dataset with 764 training images and 102 testing images. A deep convolutional neural network with 23 layers is configured, trained with a batch size of 10 over ten epochs, and employs the YOLO method to identify objects in images and subsequently detect helmetless riders. Accuracy assessment is carried out using the mean Average Precision (mAP) method, resulting in a notable 82.81% detection accuracy for riders without helmets and 75.78% for helmeted riders. The overall mAP score is 79.29%, emphasizing the system's potential to substantially improve road safety and law enforcement effort

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

Real-Time Detection of Motorcyclist without Helmet using Cascade of CNNs on Edge-device

The real-time detection of traffic rule violators in a city-wide surveillance network is a highly desirable but challenging task because it needs to perform computationally complex analytics on the live video streams from large number of cameras, simultaneously. In this paper, we propose an efficient framework using edge computing to deploy a system for automatic detection of bike-riders without helmet. First, we propose a novel robust and compact method for the detection of the motorcyclists without helmet using convolutional neural networks (CNNs). Then, we scale it for the real-time performance on an edge-device by dropping redundant filters and quantizing the model weights. To reduce the network latency, we place the detector module on edge-devices in the cameras. The edge-nodes send their detected alerts to a central alert database where the end users access these alerts through a web interface. To evaluate the proposed method, we collected two datasets of real traffic videos, namely, IITH-Helmet-1 which contains sparse traffic and IITH-Helmet-2 which contains dense traffic. The experimental results show that our method achieves a high detection accuracy of˜95% while maintaining the real-time processing speed of ˜22fps on NVIDIA-TXI. © 2020 IEEE

SCALABALE AND DISTRIBUTED METHODS FOR LARGE-SCALE VISUAL COMPUTING

The objective of this research work is to develop efficient, scalable, and distributed methods to meet the challenges associated with the processing of immense growth in visual data like images, videos, etc. The motivation stems from the fact that the existing computer vision approaches are computation intensive and cannot scale-up to carry out analysis on the large collection of data as well as to perform the real-time inference on the resourceconstrained devices. Some of the issues encountered are: 1) increased computation time for high-level representation from low-level features, 2) increased training time for classification methods, and 3) carry out analysis in real-time on the live video streams in a city-scale surveillance network. The issue of scalability can be addressed by model approximation and distributed implementation of computer vision algorithms. But existing scalable approaches suffer from the high loss in model approximation and communication overhead. In this thesis, our aim is to address some of the issues by proposing efficient methods for reducing the training time over large datasets in a distributed environment, and for real-time inference on resource-constrained devices by scaling-up computation-intensive methods using the model approximation. A scalable method Fast-BoW is presented for reducing the computation time of bagof-visual-words (BoW) feature generation for both hard and soft vector-quantization with time complexities O(|h| log2 k) and O(|h| k), respectively, where |h| is the size of the hash table used in the proposed approach and k is the vocabulary size. We replace the process of finding the closest cluster center with a softmax classifier which improves the cluster boundaries over k-means and can also be used for both hard and soft BoW encoding. To make the model compact and faster, the real weights are quantized into integer weights which can be represented using few bits (2 − 8) only. Also, on the quantized weights, the hashing is applied to reduce the number of multiplications which accelerate the entire process. Further the effectiveness of the video representation is improved by exploiting the structural information among the various entities or same entity over the time which is generally ignored by BoW representation. The interactions of the entities in a video are formulated as a graph of geometric relations among space-time interest points. The activities represented as graphs are recognized using a SVM with low complexity graph kernels, namely, random walk kernel (O(n3)) and Weisfeiler-Lehman kernel (O(n)). The use of graph kernel provides robustness to slight topological deformations, which may occur due to the presence of noise and viewpoint variation in data. The further issues such as computation and storage of the large kernel matrix are addressed using the Nystrom method for kernel linearization. The second major contribution is in reducing the time taken in learning of kernel supvi port vector machine (SVM) from large datasets using distributed implementation while sustaining classification performance. We propose Genetic-SVM which makes use of the distributed genetic algorithm to reduce the time taken in solving the SVM objective function. Further, the data partitioning approaches achieve better speed-up than distributed algorithm approaches but invariably leads to the loss in classification accuracy as global support vectors may not have been chosen as local support vectors in their respective partitions. Hence, we propose DiP-SVM, a distribution preserving kernel SVM where the first and second order statistics of the entire dataset are retained in each of the partitions. This helps in obtaining local decision boundaries which are in agreement with the global decision boundary thereby reducing the chance of missing important global support vectors. Further, the task of combining the local SVMs hinder the training speed. To address this issue, we propose Projection-SVM, using subspace partitioning where a decision tree is constructed on a projection of data along the direction of maximum variance to obtain smaller partitions of the dataset. On each of these partitions, a kernel SVM is trained independently, thereby reducing the overall training time. Also, it results in reducing the prediction time significantly. Another issue addressed is the recognition of traffic violations and incidents in real-time in a city-scale surveillance scenario. The major issues are accurate detection and real-time inference. The central computing infrastructures are unable to perform in real-time due to large network delay from video sensor to the central computing server. We propose an efficient framework using edge computing for deploying large-scale visual computing applications which reduces the latency and the communication overhead in a camera network. This framework is implemented for two surveillance applications, namely, motorcyclists without a helmet and accident incident detection. An efficient cascade of convolutional neural networks (CNNs) is proposed for incrementally detecting motorcyclists and their helmets in both sparse and dense traffic. This cascade of CNNs shares common representation in order to avoid extra computation and over-fitting. The accidents of the vehicles are modeled as an unusual incident. The deep representation is extracted using denoising stacked auto-encoders trained from the spatio-temporal video volumes of normal traffic videos. The possibility of an accident is determined based on the reconstruction error and the likelihood of the deep representation. For the likelihood of the deep representation, an unsupervised model is trained using one class SVM. Also, the intersection points of the vehicle’s trajectories are used to reduce the false alarm rate and increase the reliability of the overall system. Both the approaches are evaluated on the real traffic videos collected from the video surveillance network of Hyderabad city in India. The experiments on the real traffic videos demonstrate the efficacy of the proposed approache

Recreational Noise Exposures at Motorsport Events

Noise induced hearing loss (NIHL) is a type of hearing loss that after repeated exposure to high levels of sound for extended periods of time can result in damage to the fragile structures of the inner ear. Hearing changes caused by NIHL could be temporary or permanent. Hazardous noise levels in the workplace environment have been known to cause NIHL over time. Therefore, governing agencies have noise standards that have been enacted to decrease the risk of developing NIHL in the workplace. The Occupational Safety and Health Administration, Mine Safety and Health Administration, and the Federal Railroad Administration are examples of these governing agencies. In addition, the National Institute for Occupational Safety and Health and the World Health Organization have provided best practice guidelines for prevention of NIHL by establishing permissible noise exposure criteria. Hazardous noise exposure is not only confined to the workplace but can be found in a variety of non-occupational settings including recreational activities. Occupational and non-occupational noise exposures are cumulative throughout a lifetime. However, there are no limits to reference for recreational exposures. Common recreational activities include target shooting, attending concerts, and attending motorsport or other sporting events among others. The motorsport industry is growing and includes countless aficionados partaking in motorsport events. Both recreational and occupational noise exposure studies have documented the risk for NIHL among individuals involved in motorsports. The range of sound pressure levels were between 63 dBA to over 100 dBA across studies investigating motorcycles, snowmobiles, stock cars, F1, monster trucks and tractor pulls (Bess & Poynor, 1974; Buhr-Lawler, 2017; Dolder et al., 2013; Jordan et al., 2004; Kardous & Morata, 2010; Moore, 2014; Morley et al., 1999; Rose et al., 2008; Ross, 1989; Van Campen et al., 2005). Hot rodding is a unique motorsport among other types that has not been evaluated for noise exposure of drivers, spectators, and event personnel. Due to an increasing number of individuals with NIHL, it is important that additional research is conducted to evaluate the noise exposures from motorsports that are contributing to this health issue. Development of prevention strategies and hearing conservation programs for individuals involved in motorsports is warranted. Recommendations for future directions and hearing health promotion activities targeting this population are provided. Audiologists can play a key role in the prevention of NIHL resulting from motorsport noise exposure by providing hearing conservation services, monitoring for auditory damage, and education for motorsport enthusiasts and employers

Brain injury mitigation effects of novel helmet technologies in oblique impacts

Cyclists are a rapidly growing group of the world population, particularly after the COVID-19 pandemic which made cycling an attractive form of active mobility for commuters. Yet, cyclists are among the most vulnerable road users. Their severe injury and fatality rate per passenger mile are several folds larger than car occupants and bus passengers. Analysis of accident data shows that impacts to a cyclist’s head occur at an angle in vast majority of real-world head collisions. This produces large rotational head motion. There is significant body of research that shows rotational head motion is the key determinant of brain deformation and subsequent damage to the brain tissue. Hence, novel helmet designs adopt shear-compliant layers within a helmet with the aim of reducing the rotational head acceleration and velocity during an impact, hence reducing risk of brain injury. Cellular materials can be engineered to have interesting mechanical properties such as negative Poisson ratio or anisotropy. Their cellular structure gives rise to a unique combination of properties which are exploited in engineering design: their low density makes them ideal for light-weight design, and their ability to undergo large deformations at relatively low stresses make them ideal for dissipating kinetic energy with near-optimal deceleration. As revealed in this thesis, it also is possible to engineer cellular structures to have high or low shear stiffness with minimal change to their axial stiffness, and vice versa. This has the potential to be very beneficial for cases that require oblique impact management where both axial and shear stiffnesses play a role. However, this domain has seldom been explored, let alone applied to a use case which may result in improved performance that saves lives such as helmets. The main question this thesis aims to address is: Can helmets be improved to reduce the risk of cyclist brain injury in oblique impacts? To answer this question, it was necessary to first assess conventional helmets and emerging technologies aiming to improve helmets in oblique impacts. Hence, 27 bicycle helmets with various technologies were assessed in three different oblique impact conditions. The outcome of studying this proved that helmets may be improved with shear compliant mechanisms between the head and helmet. However, the improvements were marginal and highly dependent on impact site. This is hypothesised to be due to the presence of expanded polystyrene (EPS) foam alongside these shear-compliant mechanisms which hinders their performance. We found that one of the best performing helmets in oblique impacts was one that utilises air and entirely replaces EPS foam yet had some drawbacks such as lack of reusability and shell structure. This encouraged the work that followed which aimed to replace the EPS foam layer in helmets with an air-filled rate-sensitive cellular structure. This work leveraged finite element modelling which employed visco-hyperelastic material models which were validated with axial and oblique impact tests of the bulk material and cellular array samples different speeds. The novelty is that the axial and shear stiffness of the cells could be tailored independently with simple changes to the geometry of the cells. This led to an exciting investigation to determine whether shear-compliant cells outperformed their shear-noncompliant counterparts, which exhibit similar axial stiffness, with respect to brain injury metrics in a helmet. The results showed that, although this may be the case, often the shear-compliant cells dissipated less energy during impact and bottomed-out as a result, leading to adverse effects. Hence, introduction of shear-complaint structures in helmets should be done with care as the energy is dissipated in shear with such cellular structures during oblique impacts which needs to be properly managed. In future, the performance improvements may be implemented for different impact speeds utilising the viscoelastic nature of the cells and inflation of the cells to change their shape.Open Acces

Promoting Bicycle Commuter Safety, Research Report 11-08

We present an overview of the risks associated with cycling to emphasize the need for safety. We focus on the application of frameworks from social psychology to education, one of the 5 Es—engineering, education, enforcement, encouragement, and evaluation. We use the structure of the 5 Es to organize information with particular attention to engineering and education in the literature review. Engineering is essential because the infrastructure is vital to protecting cyclists. Education is emphasized since the central focus of the report is safety