Detection and segmentation of occluded vehicles based on symmetry analysis

Ma, Xiubo; Sun, Xiaoping

doi:10.1109/icsai.2017.8248385

Cited by 5 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To facilitate the design of the yaw angle decoding network, the size of the information matrix must be fixed. Due to the self-occlusion of the vehicle [ 47 , 48 ], the maximum number of parts that can be observed at one time is about six, so we designed the following method to fix the information matrix at a size of

. For

empty part codes are added,

, at the end.…”

Section: Yaenmentioning

confidence: 99%

A Deep Learning Framework for Accurate Vehicle Yaw Angle Estimation from a Monocular Camera Based on Part Arrangement

Huang

Tang

et al. 2022

Sensors

View full text Add to dashboard Cite

An accurate object pose is essential to assess its state and predict its movements. In recent years, scholars have often predicted object poses by matching an image with a virtual 3D model or by regressing the six-degree-of-freedom pose of the target directly from the pixel data via deep learning methods. However, these approaches may ignore a fact that was proposed in the early days of computer vision research, i.e., that object parts are strongly represented in the object pose. In this study, we propose a novel and lightweight deep learning framework, YAEN (yaw angle estimation network), for accurate object yaw angle prediction from a monocular camera based on the arrangement of parts. YAEN uses an encoding–decoding structure for vehicle yaw angle prediction. The vehicle part arrangement information is extracted by the part-encoding network, and the yaw angle is extracted from vehicle part arrangement information by the yaw angle decoding network. Because vehicle part information is refined by the encoder, the decoding network structure is lightweight; the YAEN model has low hardware requirements and can reach a detection speed of 97FPS on a 2070s graphics cards. To improve the performance of our model, we used asymmetric convolution and SSE (sum of squared errors) loss functions of adding the sign. To verify the effectiveness of this model, we constructed an accurate yaw angle dataset under real-world conditions with two vehicles equipped with high-precision positioning devices. Experimental results prove that our method can achieve satisfactory prediction performance in scenarios in which vehicles do not obscure each other, with an average prediction error of less than 3.1° and an accuracy of 96.45% for prediction errors of less than 10° in real driving scenarios.

show abstract

. For

empty part codes are added,

, at the end.…”

Section: Yaenmentioning

confidence: 99%

A Deep Learning Framework for Accurate Vehicle Yaw Angle Estimation from a Monocular Camera Based on Part Arrangement

Huang

Tang

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Adaptive thresholds will be required for different environments. Moreover, the candidate vehicle region associated with the shadow needs to be verified by symmetry [ 32 , 33 ] or training data. In [ 34 ], the feature matching and 3D feature clustering are used to select reliable features for a stereo-based technique.…”

Section: Related Workmentioning

confidence: 99%

A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

Lin

Dai

et al. 2020

Sensors

View full text Add to dashboard Cite

One major concern in the development of intelligent vehicles is to improve the driving safety. It is also an essential issue for future autonomous driving and intelligent transportation. In this paper, we present a vision-based system for driving assistance. A front and a rear on-board camera are adopted for visual sensing and environment perception. The purpose is to avoid potential traffic accidents due to forward collision and vehicle overtaking, and assist the drivers or self-driving cars to perform safe lane change operations. The proposed techniques consist of lane change detection, forward collision warning, and overtaking vehicle identification. A new cumulative density function (CDF)-based symmetry verification method is proposed for the detection of front vehicles. The motion cue obtained from optical flow is used for overtaking detection. It is further combined with a convolutional neural network to remove repetitive patterns for more accurate overtaking vehicle identification. Our approach is able to adapt to a variety of highway and urban scenarios under different illumination conditions. The experiments and performance evaluation carried out on real scene images have demonstrated the effectiveness of the proposed techniques.

show abstract

“…In addition, with the rapid advancement in autonomous driving, more related research topics have been investigated [21], [2], [35], [29]. However, there are only a few recent works focusing on occluded vehicles, including vehicle detection under occlusion [52], [28], [4] and vehicle tracking with occlusions [32], [53]. Different from prior works, our work focuses on image-based occluded vehicle appearance recovery.…”

Section: Related Workmentioning

confidence: 99%

Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery

Yan

Wang

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

View full text Add to dashboard Cite

In this paper, we propose a novel iterative multi-task framework to complete the segmentation mask of an occluded vehicle and recover the appearance of its invisible parts. In particular, to improve the quality of the segmentation completion, we present two coupled discriminators and introduce an auxiliary 3D model pool for sampling authentic silhouettes as adversarial samples. In addition, we propose a two-path structure with a shared network to enhance the appearance recovery capability. By iteratively performing the segmentation completion and the appearance recovery, the results will be progressively refined. To evaluate our method, we present a dataset, the Occluded Vehicle dataset, containing synthetic and real-world occluded vehicle images. We conduct comparison experiments on this dataset and demonstrate that our model outperforms the state-of-theart in tasks of recovering segmentation mask and appearance for occluded vehicles. Moreover, we also demonstrate that our appearance recovery approach can benefit the occluded vehicle tracking in real-world videos.

show abstract

Detection and segmentation of occluded vehicles based on symmetry analysis

Cited by 5 publications

References 11 publications

A Deep Learning Framework for Accurate Vehicle Yaw Angle Estimation from a Monocular Camera Based on Part Arrangement

A Deep Learning Framework for Accurate Vehicle Yaw Angle Estimation from a Monocular Camera Based on Part Arrangement

A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery

Contact Info

Product

Resources

About