Seat Belt Detection Research Articles

Raspberry-Pi Based Secure Systems in Car

Automotive accidents leads to not only mortality of people but also causes financial cost to both people and society. This special issue is focused on reviewing history and background of automotive system by presenting recent developments. This paper aims at giving an overview of implementing safety and security system in car for today and future development. The key enabling technology for active safety system includes drowsiness detection and Anti pinch window’s effect. Other proposed concepts to be implemented are seat belt detection and alcohol detection. Driver’s drowsiness is monitored using technique called image processing and it is processed through MATLAB but the main constraint in this technique is that processing speed on hardware. The idea proposed in this paper is that Open CV library is used for real time facial images analysis to warn the drowsiness of driver in order to prevent traffic accidents. Anti-pinch window system helps to avoid injuries because of power window. Seat belt alert system gives an audio output to wear seat belts for safety. Finally Alcohol detection detects a drunken driver and it will send a message to the nearby police station along with the details of the vehicle and its position. The proposed system does all the above features with the help of a Raspberry-Pi, camera and few sensors in its hard ware part along with coding computations through python which has the advantage of continuous monitoring at minimal cost thereby preventing accidental injuries to a great extent.

Detection and Implementation of Driver’s Seatbelt Based on FPGA

In this paper, the driver’s seatbelt detection algorithm is transplanted to the PYNQ embedded platform of XILINX to meet the technical requirements of automatic identification and detection of whether the driver wears a seatbelt or not. In this paper, aiming at the characteristics of the hardware of the FPGA, the driver’s seatbelt detection algorithm is realized by IP core from XILINX open source. Vehicle detection is realized by using YOLO object detection algorithm with 3 bit weight and 1 bit activation, and driver’s seatbelt classification is realized by using binary model with 1 bit weight and 1 bit activation. On the PYNQ embedded hardware of XILINX, the acceleration of the algorithm is realized by calling the hardware of FPGA on the ARM side.

A new method of vision-based seat belt detection

In the traffic management system, it can greatly improve the management efficiency if the algorithm that monitors automatically detects whether the driver fastens the seat belt; however, currently prevalent detecting methods cannot achieve satisfactory results in aspects of the detecting rate, the image quality requirement and the colour difference between seat belt and the surrounding environment. We propose a method of seat belt detection based on visual positioning. The algorithm locates the window according to the licence plate position and the contour statistics obtained from the gradient. The face detection is used to adjust and determine the seat belt detection area in the window. Finally, the method of seat belt detection based on the connected area is used to detect whether the seat belt is fastened. Experiments show that the successful rate of the proposed method is much higher than other existing methods, and satisfactory results are obtained.

A new method of vision-based seat belt detection

In the traffic management system, it can greatly improve the management efficiency if the algorithm that monitors automatically detects whether the driver fastens the seat belt; however, currently prevalent detecting methods cannot achieve satisfactory results in aspects of the detecting rate, the image quality requirement and the colour difference between seat belt and the surrounding environment. We propose a method of seat belt detection based on visual positioning. The algorithm locates the window according to the licence plate position and the contour statistics obtained from the gradient. The face detection is used to adjust and determine the seat belt detection area in the window. Finally, the method of seat belt detection based on the connected area is used to detect whether the seat belt is fastened. Experiments show that the successful rate of the proposed method is much higher than other existing methods, and satisfactory results are obtained.

Driver Seat Belt Detection Based on YOLO Detection and Semantic Segmentation

Driver Seat Belt Detection Based on YOLO Detection and Semantic Segmentation

Analysis and research on the images of drivers and passengers wearing seat belt in traffic inspection

It is common to see drivers and passengers not wearing seat belt in China, which is a traffic violation, thereby to strengthen supervision by resorting to advanced technical means is imminent. In the existing research, the detection effect is favorable when there is visible difference between the seat belt and the background image. However, in the opposing situation, it can easily lead to missed detection or mis-detection, and greatly reducing the detection accuracy and making it difficult to meet the demands of intelligent and automatic traffic monitoring. Study on the images of drivers and passengers wearing seat belt in traffic inspection covers three aspects: vehicle target positioning, driver and passenger area positioning and seat belt wearing behavior decision. Vehicle target positioning studies the enhanced vehicle features and looks for a vehicle classification model that is suitable for enhancing the features, and obtains the determined vehicle area from the study detection. Driver and passenger area positioning studies a multi-level joint supervised learning method according to the inclusive relationship between the non-rigid human body and the rigid vehicle body, and gets the optimal driver and passenger area by designing the multi-level learning classifier. Seat belt wearing behavior decision studies from the method of linear target recognition and uses the perceptual grouping mechanism of human visual system for robust seat belt detection. In the experimental phase, we validated the validity and accuracy, and the experiment images were from the practical traffic junctions. The results show that the method proposed in this paper can carry out Seat Belt detection accurately and effectively. Compared with other Seat Belt target detection methods, it has high detection rate and low false positive rate, which can achieve the accurate detection of Seat Belt in intelligent transportation.

Accurate seat belt detection in road surveillance images based on CNN and SVM

Seat belt detection in intelligent transportation systems is an important research area, but the current algorithms for such systems are not very well developed. Existing methods are mostly based on edge detection and the Hough transform. However, there are many kinds of vehicles and background environments, which produce many possible edges; thus, these methods often produce false positives. We therefore propose a seat belt detection algorithm for complex road backgrounds based on multi-scale feature extraction using deep learning. We first extract multi-scale features from the regions of the labeled vehicle, windshield, and seat belt to train the detection models using convolution neural network (CNN). Then the coarse candidates of the vehicle, windshield, and seat belt in the test image are detected. For the accurate detection results, a post-processing is employed by using the detection scores as well as the relative positions of these vehicle components to train a classification model through support vector machine (SVM). Finally, we perform a fine mapping and identification process using this classification model on the seat belt region. This method performed well when applied to a database of images collected by road surveillance cameras.