Learning to tell brake and turn signals in videos using CNN-LSTM structure

Hsu, Han-Kai; Tsai, Yi-Hsuan; Mei, Xue; Lee, Kuan‐Hui; Nagasaka, Naoki; Prokhorov, Danil; Yang, Ming–Hsuan

doi:10.1109/itsc.2017.8317782

Cited by 22 publications

(9 citation statements)

References 18 publications

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“…We evaluate the proposed method on UCMerced's Vehicle Rear Signal Dataset [5] [36], which contains 649 videos including 63,637 frames. The sequences are recorded during the daytime under real-world driving conditions with various vehicle types.…”

Section: Resultsmentioning

confidence: 99%

“…The I3D is embedded with Inception-v1. Moreover, we also compare to a CNN+LSTM based method [5]. For fair comparison, all the inputs are the first frame and the frame difference obtained by Eq.…”

Section: A Quantitative Resultsmentioning

confidence: 99%

“…Wang et al [4] train a CNN model from vehicle rear appearances to tell the state of the brake signals image by image. In order to take temporal dependencies into account, Hsu et al [5] propose a CNN-LSTM structure to learn eight states of taillights, where the networks for brake and turn signals are trained separately.…”

Section: A Vehicle Taillight Recognitionmentioning

confidence: 99%

“…{kuan.lee, takaaki.tagawa, marcus.pan, adrien.gaidon, bertrand.douillard}@tri.global fusion architecture is proposed to fuse spatial and temporal dependencies together [18] [19]. Such deep learning methods have achieved promising results, inspiring the researchers to apply the ideas and the techniques to vehicle taillight recognition [4] [5].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Attention-based Recurrent Convolutional Network for Vehicle Taillight Recognition

Lee¹,

Tagawa²,

Pan³

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Vehicle taillight recognition is an important application for automated driving, especially for intent prediction of ado vehicles and trajectory planning of the ego vehicle. In this work, we propose an end-to-end deep learning framework to recognize taillights, i.e. rear turn and brake signals, from a sequence of images. The proposed method starts with a Convolutional Neural Network (CNN) to extract spatial features, and then applies a Long Short-Term Memory network (LSTM) to learn temporal dependencies. Furthermore, we integrate attention models in both spatial and temporal domains, where the attention models learn to selectively focus on both spatial and temporal features. Our method is able to outperform the state of the art in terms of accuracy on the UC Merced Vehicle Rear Signal Dataset, demonstrating the effectiveness of attention models for vehicle taillight recognition.

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Section: Resultsmentioning

confidence: 99%

Section: A Quantitative Resultsmentioning

confidence: 99%

Section: A Vehicle Taillight Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Attention-based Recurrent Convolutional Network for Vehicle Taillight Recognition

Lee¹,

Tagawa²,

Pan³

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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“…The state of the turn signals is also classified via a logic on the change of luminance on either side of the vehicle. Hsu et al [22] propose the use of SIFT flow to align a sequence of images as a pre-processing step to compute a difference image, which is then used by a CNN-LSTM architecture to predict both turn signal state as well as brake lights. These methods however use manually engineered features that do not adapt to different viewpoints of the vehicles (front, left, right), and testing considers a limited set of vehicles.…”

Section: Related Workmentioning

confidence: 99%

DeepSignals: Predicting Intent of Drivers Through Visual Signals

Frossard

Kee

Urtasun

2019

2019 International Conference on Robotics and Automation (ICRA)

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Detecting the intention of drivers is an essential task in self-driving, necessary to anticipate sudden events like lane changes and stops. Turn signals and emergency flashers communicate such intentions, providing seconds of potentially critical reaction time. In this paper, we propose to detect these signals in video sequences by using a deep neural network that reasons about both spatial and temporal information. Our experiments on more than a million frames show high per-frame accuracy in very challenging scenarios.

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Vehicle Taillight Detection Based on Semantic Information Fusion

Zhang

2021

Communications in Computer and Information Science

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Learning to tell brake and turn signals in videos using CNN-LSTM structure

Cited by 22 publications

References 18 publications

An Attention-based Recurrent Convolutional Network for Vehicle Taillight Recognition

An Attention-based Recurrent Convolutional Network for Vehicle Taillight Recognition

DeepSignals: Predicting Intent of Drivers Through Visual Signals

Vehicle Taillight Detection Based on Semantic Information Fusion

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