Real-Time Vehicle Detection using a Single Rear Camera for a Blind Spot Warning System

2012 ◽  
Vol 5 (1) ◽  
pp. 146-153 ◽  
Author(s):  
Chikao Tsuchiya ◽  
Shinya Tanaka ◽  
Hiroyuki Furusho ◽  
Kenji Nishida ◽  
Takio Kurita
Keyword(s):  
Real Time ◽  
Warning System ◽  
Vehicle Detection ◽  
Blind Spot

Real-Time Side-Rear Vehicle Detection Algorithm for Blind Spot Warning Systems

2017 ◽  
Vol 23 (7) ◽  
pp. 408-416
Author(s):  
Hyunwoo Kang ◽  
Jang Woon Baek ◽  
Byung-Gil Han ◽  
Yoonsu Chung
Keyword(s):  
Real Time ◽  
Vehicle Detection ◽  
Blind Spot ◽  
Detection Algorithm ◽  
Warning Systems

scholarly journals A Real-Time Embedded Blind Spot Safety Assistance System

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Bing-Fei Wu ◽  
Chih-Chung Kao ◽  
Ying-Feng Li ◽  
Min-Yu Tsai
Keyword(s):  
Real Time ◽  
Detection System ◽  
Vehicle Detection ◽  
Blind Spot ◽  
Assistance System ◽  
Night Time ◽  
Spot Area ◽  
Temporal Features ◽  
Detection Approach ◽  
Edge Features

This paper presents an effective vehicle and motorcycle detection system in the blind spot area in the daytime and nighttime scenes. The proposed method identifies vehicle and motorcycle by detecting the shadow and the edge features in the daytime, and the vehicle and motorcycle could be detected through locating the headlights at nighttime. First, shadow segmentation is performed to briefly locate the position of the vehicle. Then, the vertical and horizontal edges are utilized to verify the existence of the vehicle. After that, tracking procedure is operated to track the same vehicle in the consecutive frames. Finally, the driving behavior is judged by the trajectory. Second, the lamps in the nighttime are extracted based on automatic histogram thresholding, and are verified by spatial and temporal features to against the reflection of the pavement. The proposed real-time vision-based Blind Spot Safety-Assistance System has implemented and evaluated on a TI DM6437 platform to perform the vehicle detection on real highway, expressways, and urban roadways, and works well on sunny, cloudy, and rainy conditions in daytime and night time. Experimental results demonstrate that the proposed vehicle detection approach is effective and feasible in various environments.

scholarly journals Real-time vision-based blind spot warning system: Experiments with motorcycles in daytime/nighttime conditions

2013 ◽  
Vol 14 (1) ◽  
pp. 113-122 ◽  
Author(s):  
C. Fernández ◽  
D. F. Llorca ◽  
M. A. Sotelo ◽  
I. G. Daza ◽  
A. M. Hellín ◽  
...  
Keyword(s):  
Real Time ◽  
Warning System ◽  
Blind Spot

scholarly journals Blind Spot Warning System based on Vehicle Analysis in Stream Images by a Real-Time Self-Supervised Deep Learning Model

2021 ◽  
Author(s):  
Arash Pourhasan Nezhad ◽  
Mehdi Ghatee ◽  
Hedieh Sajedi
Keyword(s):  
Real Time ◽  
Intelligent Systems ◽  
Warning System ◽  
Blind Spot ◽  
Percentage Error ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Real Time System ◽  
Assistance Systems ◽  
Blind Spots

<p>With the advent of intelligent systems, we are still facing a high number of fatal traffic accidents. Driver assistance systems can significantly reduce this rate. For example, when a driver uses a turn signal, driver assistance systems alert the object's presence in blind spot areas. Camera-based driver assistance systems for blind spots usually alert by detecting objects, including vehicles, in image frames. Based on a more dynamic dangerous situation classification for lane changing and turning to the sides, we propose an efficient blind-spot warning system that works with a single camera sensor for each side. Our contribution consists of two sections. First, we take a deeper look at classifying dangerous and safe situations in a dynamic environment with moving objects. Second, to distinguish dangerous situations from safe conditions, we install a pre-trained SOTA object detector to track vehicles in consecutive frames and then estimate the distances of tracked cars by a 6% mean percentage error rate. In addition, to detect objects in blind spots, the proposed system uses cars' relative velocity to warn dangerous situations. This classification process is not real-time. So, in the second section, we propose a tiny model as a driver assistance system for the blind spot that works in real-time. This tiny model feeds optical flow into CNN layers. This vision-based system uses self-supervised learning without the necessity of the labeled data. It shows 97% accuracy and can detect dangerous situations as a real-time system.</p>

scholarly journals Blind Spot Warning System based on Vehicle Analysis in Stream Images by a Real-Time Self-Supervised Deep Learning Model

2021 ◽  
Author(s):  
Arash Pourhasan Nezhad ◽  
Mehdi Ghatee ◽  
Hedieh Sajedi
Keyword(s):  
Real Time ◽  
Intelligent Systems ◽  
Warning System ◽  
Blind Spot ◽  
Percentage Error ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Real Time System ◽  
Assistance Systems ◽  
Blind Spots

<p>With the advent of intelligent systems, we are still facing a high number of fatal traffic accidents. Driver assistance systems can significantly reduce this rate. For example, when a driver uses a turn signal, driver assistance systems alert the object's presence in blind spot areas. Camera-based driver assistance systems for blind spots usually alert by detecting objects, including vehicles, in image frames. Based on a more dynamic dangerous situation classification for lane changing and turning to the sides, we propose an efficient blind-spot warning system that works with a single camera sensor for each side. Our contribution consists of two sections. First, we take a deeper look at classifying dangerous and safe situations in a dynamic environment with moving objects. Second, to distinguish dangerous situations from safe conditions, we install a pre-trained SOTA object detector to track vehicles in consecutive frames and then estimate the distances of tracked cars by a 6% mean percentage error rate. In addition, to detect objects in blind spots, the proposed system uses cars' relative velocity to warn dangerous situations. This classification process is not real-time. So, in the second section, we propose a tiny model as a driver assistance system for the blind spot that works in real-time. This tiny model feeds optical flow into CNN layers. This vision-based system uses self-supervised learning without the necessity of the labeled data. It shows 97% accuracy and can detect dangerous situations as a real-time system.</p>

Safety Monitoring and Warning System for Subway Construction Workers using Wearable Technology

2019 ◽  
Vol 13 ◽  
Author(s):  
Jun-hua Chen ◽  
Da-hu Wang ◽  
Cun-yuan Sun
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Early Warning ◽  
Warning System ◽  
Safety Monitoring ◽  
Wearable Technology ◽  
Construction Workers ◽  
Great Success ◽  
Subway Construction ◽  
Monitoring And Early Warning

Objective: This study focused on the application of wearable technology in the safety monitoring and early warning for subway construction workers. Methods: With the help of real-time video surveillance and RFID positioning which was applied in the construction has realized the real-time monitoring and early warning of on-site construction to a certain extent, but there are still some problems. Real-time video surveillance technology relies on monitoring equipment, while the location of the equipment is fixed, so it is difficult to meet the full coverage of the construction site. However, wearable technologies can solve this problem, they have outstanding performance in collecting workers’ information, especially physiological state data and positioning data. Meanwhile, wearable technology has no impact on work and is not subject to the inference of dynamic environment. Results and conclusion: The first time the system applied to subway construction was a great success. During the construction of the station, the number of occurrences of safety warnings was 43 times, but the number of occurrences of safety accidents was 0, which showed that the safety monitoring and early warning system played a significant role and worked out perfectly.

A Novel System for Nighttime Vehicle Detection Based on Foveal Classifiers With Real-Time Performance

2021 ◽  
pp. 1-13
Author(s):  
Andres Bell ◽  
Tomas Mantecon ◽  
Cesar Diaz ◽  
Carlos R. del-Blanco ◽  
Fernando Jaureguizar ◽  
...  
Keyword(s):  
Real Time ◽  
Vehicle Detection ◽  
Time Performance
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