Smart Accident Detection and Prevention System (SADPS)

Smart Accident Detection and Prevention System (SADPS)

Jeyabharathi D., Kesavaraja D., Sasireka D., Barkath Nisha S.
Copyright: © 2019 |Pages: 15
DOI: 10.4018/978-1-5225-7811-6.ch006
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Abstract

The two objectives of the smart accident detection and prevention system (SADPS) are 1) accident prevention and 2) accident detection. Based on the survey, 1.3 million people die every year due to roadway accidents. The main reason for this type of accident is speeding. So, the proposed SADPS focused on finding the speed parameters of each vehicle and giving notification to speeding vehicles through SMS that can be used to prevent accidents. The second objective is accident detection. For this task, each vehicle accelerometer values will be taken by the SADPS system. When an accident occurs, the location as well as the related details are sent to the SADPS system. This proposed system takes the immediate remedy by alerting the nearby police station and hospitals. Proposed SADPS also acts as a video surveillance and monitoring system. Automatic background subtraction and object tracking is done with the help of novel approaches.
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Introduction

IoT Based Accident Detection and Prevention

Due to traffic hazards and road accidents, the life of the people went under risk. This is because of the lack of best emergency facilities available in our country.

To improve this, there is a need for alert the emergency services to get the accident information as soon as possible when an accident is occurring. Overspeed of the vehicle is a main reason for accidents. To save people lives, IOT based accident detection and prevention is an essential one.

The Internet of Things (IoT) is an arrangement of interrelated computing gadgets, mechanical and digital machines, objects, animals or individuals that are given one kind of an identifiers and the capacity to exchange information over a system without requiring human to human or human.

Using IoT technique automatic accident detection and prevention system can be built. In the proposed system without using sensor automatic vehicle detection and tracking is done. For accident detection and prevention purpose IOT sensor is used.

Vision Based Vehicle Detection and Tracking

Background Subtraction (BS) plays an important role in video surveillance system because it provides a focus of attention for moving object detection. Even though numerous background subtraction algorithms have been proposed in the literature, the issue of distinguishing moving objects in challenging scenarios such as dynamic backgrounds and illumination variation is still far from being totally solved. The proposed background subtraction approaches create an accurate and adaptive background model with the help of key frame selection strategy and symmetry-based subspace construction process. So, it can handle the challenges prevailing with respect to background subtraction.

Object tracking is also one of the challenging tasks in the field of computer vision. Tracking can be defined as the problem of estimating the trajectory of an object in the image plane as it moves around the scene. The main problem prevailing even now in object tracking is occlusion handling. The proposed trackers utilize diagonal directional derivatives to extract unique features from each object capable of handling occlusion and partial occlusion successfully.

With respect to further analysis of the tracked object, in traffic video surveillance system, the speed of the vehicle has to be estimated. Vehicle speed measurement system uses sensors to measure the speed of the vehicles. It is more expensive. The proposed system estimates the speed of each vehicle from the video footage. Hence it provides a cheaper solution than other existing mechanisms and is well suited for real-time applications.

Objective

The main objective of the proposed system is twofold:

  • •

    The main objective is to identify the overspeeding vehicle as well to detect accident and find out the location for accident to take remedy.

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    The secondary objective of the proposed system is to develop novel approaches for background subtraction and tracking. The objective of the proposed background subtraction approaches is to create an accurate and adaptive background model suitable for real-time environment.

Contribution of the Book Chapter

The following are the contribution of this book chapter:

  • •

    Raspberry Pi3 sensor and Arduino Uno sensor can be used to detect and prevent accident.

  • •

    Highlighted Point Tristate Pattern (HPTP) is proposed for both background subtraction and object tracking. Most highlighted point from each spatio-temporal block is taken to create an accurate background model. So the computational complexity is reduced greatly.

  • •

    Proposed invariant Tristate pattern can be used to give an accurate object detection process.

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