Vehicle recognition and TTC estimation at night based on spotlight pairing

Feature Enhancement Based on CycleGAN for Nighttime Vehicle Detection

“…For obtaining ROIs of vehicles, the following techniques can be adopted: threshold-based segmentation methods [5], [12], [18], paired vehicle lighting-based methods [6]- [8], [14]- [16], saliency map-based methods [17], [27], and artificially designed feature-based methods [13]. After the ROIs are obtained, we need to further determine whether these candidate regions contain vehicles.…”

“…Taillights were localized by segmenting the image, and vehicle bounding boxes were predicted by assuming the typical width of the vehicle [4], [5]. Region proposals were firstly obtained by paired taillights of vehicles, next the vehicles whether in these region proposals were determined [6], [7]. In [10], a detection-by-tracking method was proposed to detect multiple vehicles by tracking headlights/taillights.…”

Comparison of Mono Camera-based Static Obstacle Position Estimation Methods for Automotive Application

“…Probably the simplest method is the one published in [6]. This estimates TTC from the scale change of the obstacle size in the camera and the sampling time (∆t) (f ps of camera) as follows:T…”

“…The author targets to compare existing mono camera-based methods without radar assistance as this is an extensively researched field in automotive ( [2], [3], [4], [5], [6], [7], [8], [9], [10], [11]) and a good opportunity to evaluate his mono camera-based obstacle avoidance algorithm ( [12], [13]) used in aerospace until now. The final goal is to select the best algorithm which can be further evaluated and possibly tested in automotive application.…”

“…Scale change of the obstacle image and system sampling time are considered in [6] and [11] to estimate time to collision (TTC). Assuming known constant velocity the range of the obstacle can be estimated and with known range the size and side distance can also be estimated as pointed out in subsection III-A.…”