Over the past decade, vision-based vehicle detection techniques for road safety improvement have gained an increasing amount of attention. Unfortunately, the techniques suffer from robustness due to huge variability in vehicle shape (particularly for motorcycles), cluttered environment, various illumination conditions, and driving behavior. In this paper, we provide a comprehensive survey in a systematic approach about the state-of-the-art on-road vision-based vehicle detection and tracking systems for collision avoidance systems (CASs). This paper is structured based on a vehicle detection processes starting from sensor selection to vehicle detection and tracking. Techniques in each process/step are reviewed and analyzed individually. Two main contributions in this paper are the following: survey on motorcycle detection techniques and the sensor comparison in terms of cost and range parameters. Finally, the survey provides an optimal choice with a low cost and reliable CAS design in vehicle industries.

Vehicle Detection Techniques for Collision Avoidance Systems: A Review

In this paper, we propose an approach for automatic detection of bike-riders without helmet using surveillance videos in real time. The proposed approach first detects bike riders from surveillance video using background subtraction and object segmentation. Then it determines whether bike-rider is using a helmet or not using visual features and binary classifier. Also, we present a consolidation approach for violation reporting which helps in improving reliability of the proposed approach. In order to evaluate our approach, we have provided a performance comparison of three widely used feature representations namely histogram of oriented gradients (HOG), scale-invariant feature transform (SIFT), and local binary patterns (LBP) for classification. The experimental results show detection accuracy of 93.80% on the real world surveillance data. It has also been shown that proposed approach is computationally less expensive and performs in real-time with a processing time of 11.58 ms per frame.

Automatic detection of bike-riders without helmet using surveillance videos in real-time

Motorcycle accidents have been rapidly growing throughout the years in many countries. Due to various social and economic factors, this type of vehicle is becoming increasingly popular. The helmet is the main safety equipment of motorcyclists, but many drivers do not use it. If an motorcyclist is without helmet an accident can be fatal. This paper aims to explain and illustrate an automatic method for motorcycles detection and classification on public roads and a system for automatic detection of motorcyclists without helmet. For this, a hybrid descriptor for features extraction is proposed based in Local Binary Pattern, Histograms of Oriented Gradients and the Hough Transform descriptors. Traffic images captured by cameras were used. The best result obtained from classification was an accuracy rate of 0.9767, and the best result obtained from helmet detection was an accuracy rate of 0.9423.

Automatic detection of motorcyclists without helmet

Helmet detection has a wide area of applications in image processing. A new helmet detection technique is proposed in this paper. The technique combines two methods for helmet detection to achieve better detection rates. The two methods are face detection using haar like feature for detection between without helmet and full helmet and circle hough transform for detection between without helmet and half helmet. In the first module of the technique, we proposed a fast algorithm for detecting helmet in color images. The algorithm uses haar like feature to detect helmet regions. That, face / nose / mouth / left eye / right eye detection method can not detection between full and half helmet. So, the second module of the technique, the circle hough transform is applied for detection it. In experiments on images, our technique has achieved high detection rates and low false positives due to the application of the new algorithm.

Helmet Wearing Detection in Thailand Using Haar Like Feature and Circle Hough Transform on Image Processing

The application such as video surveillance for traffic control in smart cities needs to analyze the large amount (hours/days) of video footage in order to locate the people who are violating the traffic rules. The traditional computer vision techniques are unable to analyze such a huge amount of visual data generated in real-time. So, there is a need for visual big data analytics which involves processing and analyzing large scale visual data such as images or videos to find semantic patterns that are useful for interpretation. In this paper, we propose a framework for visual big data analytics for automatic detection of bike-riders without helmet in city traffic. We also discuss challenges involved in visual big data analytics for traffic control in a city scale surveillance data and explore opportunities for future research.

Visual Big Data Analytics for Traffic Monitoring in Smart City

A real-time monocular vision based rear vehicle and motorcycle detection and tracking approach is presented for Lane Change Assistant (LCA). To achieve robustness and accuracy this work detects and tracks multiple vehicles and motorcycles on road by combining multiple cues. To achieve real-time multi-resolution technology is used to reduce computing complexity, and all algorithms have been implemented on an IMAP (Integrated Memory Array Processor) parallel vision board. The test results under various traffic scenes illustrated accuracy, robustness and real-time of this work.

Real-time on-road vehicle and motorcycle detection using a single camera

Robustness and real-time usually are the main challenges when designing image understanding approach for practical application. To achieve robustness, integrating multiple algorithms to make a special hybrid approach are becoming a popular way and there have been a lot of successful hybrid approaches. But this aggravates the difficulties in achieving real-time because of heavy computational workload of multiple algorithms. To design a hybrid approach with real-time constraint more easily, some theoretical researches about multiple algorithm integration are very necessary. This paper presents a common multiple algorithm integration model and architecture for typical image understanding applications. To achieve robustness and real-time in a hybrid approach, the strategies for increasing robustness and speed up are analyzed. Finally a robust hybrid approach for rear vehicle and motorcycle detection and tracking is introduced as a sample.

Architecture of Multiple Algorithm Integration for Real-Time Image Understanding Application

Current mainstream vehicle recognition algorithms mainly depend on the synthesis of both appearance based and knowledge based features to identify the candidate objects. Whereas, because of the unpredictable complex noises in real world environments, the existences, quantification and the explanation for certain features are often ambiguous which makes current algorithm hard to fulfill the dilemmatic high sensitivity/accuracy restriction, and an improvement for a certain feature(or data sets) often leads to a degeneration for others. This paper introduces a probability based feature selection method which enables the dynamic feature selection and multigrain feature evaluation. The experiment result (for rear vehicle recognition) shows the proposed method is an efficient way to improve both the sensitivity and the accuracy rates without the degeneration phenomenon.

A Dynamic Feature Selection Method for Vision Based Vehicle Recognition

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Real-time on-road vehicle and motorcycle detection using a single camera

Architecture of Multiple Algorithm Integration for Real-Time Image Understanding Application

A Dynamic Feature Selection Method for Vision Based Vehicle Recognition