Traffic Light Detection: A Learning Algorithm and Evaluations on Challenging Dataset

Abstract

Abstract-Traffic light recognition (TLR) is an integral part of any intelligent vehicle, which must function in the existing infrastructure. Pedestrian and sign detection have recently seen great improvements due to the introduction of learning based detectors using integral channel features. A similar push have not yet been seen for the detection sub-problem of TLR, where detection is dominated by methods based on heuristic models.Evaluation of existing systems is currently limited primarily to small local datasets. In order to provide a common basis for comparing future TLR research an extensive public database is collected based on footage from US roads. The database consists of both test and training data, totaling 46,418 frames and 112,971 annotated traffic lights, captured in continuous sequences under a varying light and weather conditions.The learning based detector achieves an AUC of 0.4 and 0.32 for day sequence 1 and 2, respectively, which is more than an order of magnitude better than the two heuristic model-based detectors.

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.…”

“…The parameter optimization is done by adjusting one parameter at a time, e.g. creating a TL detector with a nOctUp = 0 and treeDepth = 2, and then vary the mDs size from [12,12] to [25,25]. A total of 14 2 = 196 detectors are made with above nOctUp and treeDepth settings.…”

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

“…[10] is combining occurrence priors from a probabilistic prior map and detection scores based on SVM classification of Histogram of Oriented Gradients (HoG) features to detect TLs. [3] uses the ACF framework provided by [11]. Here features are extracted as summed blocks of pixels in 10 channels created from transformations of the original RGB frames.…”

“…The learning-based detector is provided as part of the Matlab toolbox from [11]. It is similar to the detectors seen in [17] for traffic signs and [3] for day-time TLs, except for few differences in the configuration and training which are described below.…”

“…The purpose of this paper is therefore to evaluate the night-time TLD performance of three heuristic TL detectors and compare this to a state-of-the-art learning based detector relying on Aggregated Channel Features (ACF). The same learning-based detection framework has previously been applied for day-time TLD in [3]. This makes it possible to compare the detector's performance at night and day.…”