A Novel Method of Adaptive Traffic Image Enhancement for Complex Environments

Abstract

There exist two main drawbacks for traffic images in classic image enhancement methods. First is the performance degradation that occurs under frontlight, backlight, and extremely dark conditions. The second drawback is complicated manual settings, such as transform functions and multiple parameter selection mechanisms. Thus, this paper proposes an effective and adaptive parameter optimization enhancement algorithm based on adaptive brightness baseline drift (ABBD) for color traffic images under different luminance conditions. This method consists of two parts: brightness baseline model acquisition and adaptive color image compensation. The brightness baseline model can be attained by analyzing changes in light along a timeline. The adaptive color image compensation involves color space remapping and adaptive compensation specific color components. Our experiments were tested on various traffic images under frontlight, backlight, and during nighttime. The experimental results show that the proposed method achieved better effects compared with other available methods under different luminance conditions, which also effectively reduced the influence of the weather.

A Visual Navigation Control Method of Substation Inspection Robot

“…So that the navigation path on the different road sections have the same light intensity and guarantee the consistency and stability of visual images [9] .…”

A State-of-the-Art Review of the Sensor Location, Flow Observability, Estimation, and Prediction Problems in Traffic Networks

“…In this context, the role of sensors and their location and the flow observability, estimation, and prediction problems become important. Though sensors can be used for many different purposes in the traffic field (see, e.g., Gil Jiménez and Fernández-Getino García [1], Liu et al [2], and Kong et al [3]), in this paper, we use sensors to determine the traffic flow. In particular, it is important to distinguish between passive and active sensors.…”

“…Objective function (1) minimizes the number of cameras and among all solutions chooses the one with the most observed routes. Constraint (2), if the binary variable is equal to 1, guarantees that route is able to be distinguished by the subset of scanned links from the other routes. Note that if + 1 = 1, link belongs only to one of the routes and that if ∑ ≥ and = 1, at least one scanned link has this property.…”