Robust and Computationally Lightweight Autonomous Tracking of Vehicle Taillights and Signal Detection by Embedded Smart Cameras

Almagambetov, Akhan; Velipasalar, Senem; Casares, Mauricio

doi:10.1109/tie.2015.2400420

Cited by 55 publications

(37 citation statements)

References 32 publications

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“…We show that (1) our algorithm maintains the mean classification accuracy of around 70% even when it doesn't rely on infrastructure -such as AprilTagsas an image registration method. We validate (2) in real-world experiments that our autonomous car driving in night time conditions detects a hidden approaching car earlier with our virtual sensor than with the front facing 2-D LiDAR.…”

Section: Infrastructure-free Nlos Obstacle Detection For Autonomous Carsmentioning

confidence: 60%

“…This is mostly due to active safety features such as Advanced Driver Assistance Systems (ADAS). Despite this positive trend still around 1.3M fatalities occur due to road accidents every year according to the World Health Organization (WHO) 2 . Specifically dangerous are night time driving scenarios 3 and almost half of the intersection related crashes are caused due to the driver's inadequate surveillance 4 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Infrastructure-free NLoS Obstacle Detection for Autonomous Cars

Naser

Gilitschenski

Amini

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Current perception systems mostly require direct line of sight to anticipate and ultimately prevent potential collisions at intersections with other road users. We present a fully integrated autonomous system capable of detecting shadows or weak illumination changes on the ground caused by a dynamic obstacle in NLoS scenarios. This additional virtual sensor "ShadowCam" extends the signal range utilized so far by computer-vision ADASs. We show that (1) our algorithm maintains the mean classification accuracy of around 70% even when it doesn't rely on infrastructure -such as AprilTagsas an image registration method. We validate (2) in real-world experiments that our autonomous car driving in night time conditions detects a hidden approaching car earlier with our virtual sensor than with the front facing 2-D LiDAR.

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Section: Infrastructure-free Nlos Obstacle Detection For Autonomous Carsmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Infrastructure-free NLoS Obstacle Detection for Autonomous Cars

Naser

Gilitschenski

Amini

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…A high-pass mask is used to find the brighter in-region pixels for classifying the states accordingly. Several methods [9], [10], [22] Fig. 1: Network architecture.…”

Section: A Vehicle Taillight Recognitionmentioning

confidence: 99%

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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“…The system detects and tracks vehicles while they pass through the camera, providing information such as vehicle count, average speed, or traffic congestions [5][6][7]. To achieve it, an algorithm must be created that segments and tracks the vehicle movements within the video [6,8,9].…”

Section: Introductionmentioning

confidence: 99%

An Alternative Vehicle Counting Tool Using the Kalman Filter within MATLAB

et al. 2017

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This study proposes an alternative and economical tool to estimate traffic densities, via video-image processing adapting the Kalman filter included in the Matlab code. Traffic information involves acquiring data for long periods of time at stationary points. Vehicle counting is vital in modern transport studies, and can be achieved by using different techniques, such as manual counts, use of pneumatic tubes, magnetic sensors, etc. In this research however, automatic vehicle detection was achieved using image processing, because it is an economical and sometimes even faster option. Commercial automatic vehicle detection and tracking programs/applications already exist, but their use is typically prohibitive due to their high cost. Large cities can obtain traffic recordings from surveillance cameras and process the information, but it is difficult for smaller towns without such infrastructure or even assigned budget. The proposed tool was developed taking into consideration these difficult situations, and it only requires users to have access to a fixed video camera placed at an elevated point (e.g. a pedestrian bridge or a light pole) and a computer with a powerful processor; the images are processed automatically through the Kalman filter code within Matlab. The Kalman filter predicts random signals, separates signals from random noise or detects signals with the presence of noise, minimizing the estimated error. It needs nevertheless some adjustments to focus it for vehicle counting. The proposed algorithm can thus be adapted to fit the users' necessities and even the camera's position. The use of this algorithm allows to obtain traffic data and may help small cities´ decision makers dealing with present and future urban planning and the design or installment of transportation systems.

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Robust and Computationally Lightweight Autonomous Tracking of Vehicle Taillights and Signal Detection by Embedded Smart Cameras

Cited by 55 publications

References 32 publications

Infrastructure-free NLoS Obstacle Detection for Autonomous Cars

Infrastructure-free NLoS Obstacle Detection for Autonomous Cars

An Attention-based Recurrent Convolutional Network for Vehicle Taillight Recognition

An Alternative Vehicle Counting Tool Using the Kalman Filter within MATLAB

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