A framework of traffic lights detection, tracking and recognition based on motion models

Zhang, Litian; Fu, Mengyin; Yang, Yi; Wang, Meiling

doi:10.1109/itsc.2014.6958058

Cited by 6 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GPS as an IE can be used in different ways, associated with different techniques. Some works consider the exact position of the traffic light and compare it with the position of the vehicle; in such case, previous knowledge of all traffic light positions can be used, as in the work of [46], or a traffic light mapping algorithm can be defined as in [35], [47] and [48]. To considerably decreases the amount of processing required, it is also possible to check if the vehicle is positioned close to an intersection and, just in this case, activate the traffic light recognition mechanism [16].…”

Section: Embedding Expert Instructionsmentioning

confidence: 99%

Frequency Maps as Expert Instructions to Lessen Data Dependency on Real-time Traffic Light Recognition

Macedo¹,

Almeida²,

Matos³

et al. 2020

Preprint

View full text Add to dashboard Cite

Research on Traffic Light Recognition (TLR) has grown in recent years, primarily driven by the growing interest in autonomous vehicles development. Machine Learning algorithms have been widely used to that purpose. Mainstream approaches, however, require large amount of data to properly work, and as a consequence, a lot of computational resources. In this paper we propose the use of Expert Instruction (IE) as a mechanism to reduce the amount of data required to provide accurate ML models for TLR. Given an image of the exterior scene taken from the inside of the vehicle, we stand the hypothesis that the picture of a traffic light is more likely to appear in the central and upper regions of the image. Frequency Maps of traffic light location were thus constructed to confirm this hypothesis. The frequency maps are the result of a manual effort of human experts in annotating each image with the coordinates of the region where the traffic light appears. Results show that EI increased the accuracy obtained by the classification algorithm in two different image datasets by at least 15%. Evaluation rates achieved by the inclusion of EI were also higher in further experiments, including traffic light detection followed by classification by the trained algorithm. The inclusion of EI in the PCANet achieved a precision of 83% and recall of 73% against 75.3% and 51.1%, respectively, of its counterpart. We finally presents a prototype of a TLR Device with that expert model embedded to assist drivers. The TLR uses a smartphone as a camera and processing unit. To show the feasibility of the apparatus, a dataset was obtained in real time usage and tested in an Adaptive Background Suppression Filter (AdaBSF) and Support Vector Machines (SVMs) algorithm to detect and recognize traffic lights. Results show precision of 100% and recall of 65%.

show abstract

Section: Embedding Expert Instructionsmentioning

confidence: 99%

Frequency Maps as Expert Instructions to Lessen Data Dependency on Real-time Traffic Light Recognition

Macedo¹,

Almeida²,

Matos³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Generic object tracking is a fundamental task in the field of computer vision, with a wide range of application needs in the fields of monitoring, automatic driving [1,2], surgical detection [3], posture recognition [4], and industrial measurement [5]. In recent years, many excellent achievements emerged in the visual object tracking task, but this task remains challenging due to the impact of external factors, such as target deformation, environmental illumination, and background disturbance.…”

Section: Introductionmentioning

confidence: 99%

Double Branch Attention Block for Discriminative Representation of Siamese Trackers

Yang

Chen

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Siamese trackers have achieved a good balance between accuracy and efficiency in generic object tracking. However, background distractors cause side effects to the discriminative representation of the target. To suppress the sensitivity of trackers to background distractors, we propose a Double Branch Attention (DBA) block and a Siamese tracker equipped with the DBA block named DBA-Siam. First, the DBA block concatenates channels of multiple layers from two branches of the Siamese framework to obtain rich feature representation. Second, the channel attention is applied to the two concatenated feature blocks to enhance the robust features selectively, thus enhancing the ability to distinguish the target from the complex background. Finally, the DBA block collects the contextual relevance between the Siamese branches and adaptively encodes it into the feature weight of the detection branch for information compensation. Ablation experiments show that the proposed block can enhance the discriminative representation of the target and significantly improve the tracking performance. Results on two popular benchmarks show that DBA-Siam performs favorably against its counterparts. Compared with the advanced algorithm CSTNet, DBA-Siam improves the EAO by 18.9% on VOT2016.

show abstract

Traffic light detection for colorblind individuals

Al-Nabulsi

Mesleh

Yunis

2017

2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)

View full text Add to dashboard Cite

A framework of traffic lights detection, tracking and recognition based on motion models

Cited by 6 publications

References 16 publications

Frequency Maps as Expert Instructions to Lessen Data Dependency on Real-time Traffic Light Recognition

Frequency Maps as Expert Instructions to Lessen Data Dependency on Real-time Traffic Light Recognition

Double Branch Attention Block for Discriminative Representation of Siamese Trackers

Traffic light detection for colorblind individuals

Contact Info

Product

Resources

About