Traffic Light Detection at Night: Comparison of a Learning-Based Detector and Three Model-Based Detectors

Abstract

Abstract. Traffic light recognition (TLR) is an integral part of any intelligent vehicle, it must function both at day and at night. However, the majority of TLR research is focused on day-time scenarios. In this paper we will focus on detection of traffic lights at night and evaluate the performance of three detectors based on heuristic models and one learning-based detector. Evaluation is done on night-time data from the public LISA Traffic Light Dataset. The learning-based detector outperforms the model-based detectors in both precision and recall. The learning-based detector achieves an average AUC of 51.4 % for the two night test sequences. The heuristic model-based detectors achieves AUCs ranging from 13.5 % to 15.0 %.

Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives

“…[58] uses the HLS color space for determining the state of found TLs amd [31] uses IHLS which is an modification of HLS, which separates chromatic and achromatic objects well. [59], [60] uses the LUV color space for extracting color features.…”

Evaluating State-of-the-Art Object Detector on Challenging Traffic Light Data

“…As for most other computer vision research areas, the popular combination of using Histogram of Oriented Gradients features together with a SVM classifier was introduced in [2]. The learning-based Aggregated Channel Features (ACF) detector have seen a large use in TLD, and have shown superior performance over the heuristic models both during day and night time [10,9]. TLD using Convolutional Neural Network (CNN) is introduced in [13,12], where a CNN is used detects and recognize the traffic lights using region-of-interest information provided by an onboard GPS sensor.…”

Comprehensive Parameter Sweep for Learning-Based Detector on Traffic Lights

“…The learning-based ACF detector has previously been used for TLs, where features are extracted as summed blocks of pixels in 10 different channels created from the original input RGB frame. In [25] and [6] the extracted features are classified using depth-2 and depth-4 decision trees, respectively. In [6] the octave parameter, which define the number of octaves to compute above the original scale, is changed from 0 to 1.…”

“…In [25] and [6] the extracted features are classified using depth-2 and depth-4 decision trees, respectively. In [6] the octave parameter, which define the number of octaves to compute above the original scale, is changed from 0 to 1.…”

“…Originally it was introduced as as a detector for pedestrians in [1], but have since been applied in several other areas related to driver assistant systems (DAS). The applied areas are not only limited to looking-out of the vehicle [3], where other vehicles [4], signs [5], and traffic lights (TLs) [6] have been popular, but also looking-in areas, such as hands detection [7] has seen use of the ACF detector. General for all areas is that the ACF object detector has been adjusted heuristically in a practical manner.…”