A Region Tracking-Based Vehicle Detection Algorithm in Nighttime Traffic Scenes

Abstract

Abstract:The preceding vehicles detection technique in nighttime traffic scenes is an important part of the advanced driver assistance system (ADAS). This paper proposes a region tracking-based vehicle detection algorithm via the image processing technique. First, the brightness of the taillights during nighttime is used as the typical feature, and we use the existing global detection algorithm to detect and pair the taillights. When the vehicle is detected, a time series analysis model is introduced to predict vehicle positions and the possible region (PR) of the vehicle in the next frame. Then, the vehicle is only detected in the PR. This could reduce the detection time and avoid the false pairing between the bright spots in the PR and the bright spots out of the PR. Additionally, we present a thresholds updating method to make the thresholds adaptive. Finally, experimental studies are provided to demonstrate the application and substantiate the superiority of the proposed algorithm. The results show that the proposed algorithm can simultaneously reduce both the false negative detection rate and the false positive detection rate.

Adaptive Traffic Controller Based On Pre-Timed System

“…It can be concluded from the previous research that the existence of vehicle would make on obvious difference between the vehicle and the background color [12] [13]. This would make the standard deviation of the intensity histogram higher.…”

The Effects of Using In-Vehicle Computer on Driver Eye Movements and Driving Performance

“…Driver distraction can be defined as the diversion of attention from the driving task as compelled by an activity or event, inside or outside the vehicle, which competes for the attention of the driver. It usually results in a delayed recognition and processing of the information needed to safely accomplish the driving task [1][2][3]. According to Nevile and Haddington's reports, drivers commonly engage in distracting activities while driving accounting for 14-21% of crashes caused by distraction [4].…”

Night Time and Low Visibility Driving Assistance Based on the Application of Colour and Geometrical Features Extraction

“…Taking into account the adverse and the low visibility conditions that the experimental environment shows, generally it is observed that the proposed solution is effective as it is completely developed to be used in real time in contrast to [7,13]. In addition, the result of precision of a 95.20 % is included in the ranks reached by [14] with the difference that the presented system is implemented on a platform with a limited resolution capacity considering that a better resolution of the optical sensor guarantees better results and it is totally accessible, compact and handheld.…”

“…The monitoring and presentation of warnings take place in real time at the rate of 10 fps. In the same way in [14] the brightness of the tail lights is used to establish the geometric relations and temporary space in the detection and tracking of cars in night time, in this case the system is not implemented in real time. In [15] the implementation of the whole net of sensors and cameras to acquire information from both the external and internal, which refers to vehicle elements, driving scenarios are shown.…”