Performance Evaluation of Region-Based Convolutional Neural Networks Toward Improved Vehicle Taillight Detection

Abstract

Increasingly serious traffic jams and traffic accidents pose threats to the social economy and human life. The lamp semantics of driving is a major way to transmit the driving behavior information between vehicles. The detection and recognition of the vehicle taillights can acquire and understand the taillight semantics, which is of great significance for realizing multi-vehicle behavior interaction and assists driving. It is a challenge to detect taillights and identify the taillight semantics on real traffic road during the day. The main research content of this paper is mainly to establish a neural network to detect vehicles and to complete recognition of the taillights of the preceding vehicle based on image processing. First, the outlines of the preceding vehicles are detected and extracted by using convolutional neural networks. Then, the taillight area in the Hue-Saturation-Value (HSV) color space are extracted and the taillight pairs are detected by correlations of histograms, color and positions. Then the taillight states are identified based on the histogram feature parameters of the taillight image. The detected taillight state of the preceding vehicle is prompted to the driver to reduce traffic accidents caused by the untimely judgement of the driving intention of the preceding vehicle. The experimental results show that this method can accurately identify taillight status during the daytime and can effectively reduce the occurrence of confused judgement caused by light interference.

A New Face Recognition Method for Intelligent Security

“…As the experiments above showed, compared to other algorithms such as ICP, SP, and LBP, this method has a relatively higher recognition rate (97.10%) on human faces with a faster speed of 2.81 s. Calculations show the method is robust to attitude, illumination, and noise and achieves a better recognition performance. It can be applied to smart cities [30] and civil security where intelligent security robots with efficient face recognition functions in the big data environment can be considered as innovations of the traditional security industry and public safety services. In the future, we hope to improve the face recognition rate and the resolution of the facial expressions, enrich the users' scenarios, and increase market acceptance.…”

A Taillight Matching and Pairing Algorithm for Stereo-Vision-Based Nighttime Vehicle-to-Vehicle Positioning

“…In our study, we wanted to construct pairingClassifier so that when given the feature vector pairVec of the two arbitrary taillights, it can determine whether these taillights belong to the same vehicle. Differently from previous works that compared the feature correlation between the two taillights with some experimental thresholds [12][13][14][15][16][17][18][19]26], we suggest applying an artificial neural network that will help to adaptively determine the sufficient threshold from the dataset. Specifically, a three-layer feed-forward neural network [34] was chosen as the architecture for pairingClassifier.…”

“…The verification phase, which consists of one step that is taillight verification, is to verify whether each candidate region is a taillight or not. The verification phase is an additional phase when compared with many existing detection methods [12,13,[15][16][17]. It should be noted that sometimes when taillights from different vehicles approach each other, taillight occlusion occasionally occur under the camera's viewpoint.…”

“…The most popular approach is relying on color and brightness, given the assumption that the vehicle's taillights dominantly contain red points and their brightness is relatively higher than the other parts of the image. Specifically, they consider the threshold based on different color spaces, such as HSV [12][13][14][15], Lab [16], Y'UV [17], grayscale [18], or RGB [19], and use some conventional techniques [12,13,[15][16][17][18][19] or machine-learning-based techniques [14] to detect the taillights from the image. However, the previous works often lacked a step to verify whether the detected region is a taillight or just a bright object.…”

“…(3) Pairing problem: Regularly, to decide whether the two taillights belong to the same vehicle, the common approach is comparing some relations relevant to their size, color, and position with some particular thresholds [12][13][14][15][16][17][18][19]26]. The threshold values are usually chosen empirically but must be reasonable to distinguish between the two taillights.…”