Moving camera background-subtraction for obstacle detection on railway tracks

Mukojima, Hiroki; Deguchi, Daisuke; Kawanishi, Yasutomo; Ide, Ichiro; Murase, Hiroshi; Ukai, Masato; Nagamine, Nozomi; Nakasone, Ryuta

doi:10.1109/icip.2016.7533104

Cited by 61 publications

(27 citation statements)

References 15 publications

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“…In addition, logistical and technical problems continue to hamper industrial development in passenger and freight transportation. Recent endeavours [1,2] did show that technology can significantly improve the competitiveness of road transport by integrating innovative services but this area is still facing real issues and important challenges. Thus, our contribution is a unified framework that works on complex road environments to improve security, but also to make the traffic more fluent.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Real-Time 3D Multi-Object Detection, Localisation, and Tracking: Application to Smart Mobility

Mauri

Khemmar

Decoux

et al. 2020

Sensors

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In core computer vision tasks, we have witnessed significant advances in object detection, localisation and tracking. However, there are currently no methods to detect, localize and track objects in road environments, and taking into account real-time constraints. In this paper, our objective is to develop a deep learning multi object detection and tracking technique applied to road smart mobility. Firstly, we propose an effective detector-based on YOLOv3 which we adapt to our context. Subsequently, to localize successfully the detected objects, we put forward an adaptive method aiming to extract 3D information, i.e., depth maps. To do so, a comparative study is carried out taking into account two approaches: Monodepth2 for monocular vision and MADNEt for stereoscopic vision. These approaches are then evaluated over datasets containing depth information in order to discern the best solution that performs better in real-time conditions. Object tracking is necessary in order to mitigate the risks of collisions. Unlike traditional tracking approaches which require target initialization beforehand, our approach consists of using information from object detection and distance estimation to initialize targets and to track them later. Expressly, we propose here to improve SORT approach for 3D object tracking. We introduce an extended Kalman filter to better estimate the position of objects. Extensive experiments carried out on KITTI dataset prove that our proposal outperforms state-of-the-art approches.

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Section: Introductionmentioning

confidence: 99%

Deep Learning for Real-Time 3D Multi-Object Detection, Localisation, and Tracking: Application to Smart Mobility

Mauri

Khemmar

Decoux

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…We compared the performance of our proposed multi-SASL to the state-of-the-art mcRoSuRe-A [7], as well as the STC-mc [17], DAOMC [11], MCBS [12], and ADMULT [10] methods, and the result is shown in Table 1. Multi-SASL method outperforms other algorithms except ADMULT method.…”

Section: Comparison Of Object Detection Methodsmentioning

confidence: 99%

“…Background modeling mainly includes initial background modeling, extracting feature points from the current frame, establishing association between feature matching and corresponding background, calculating conversion matrix between them, dividing foreground and background pixels, and updating background model. For moving camera, background modeling requires motion compensation for the uncertain relationship between the background model and the coordinates of the pixels caused by camera movement [11,12]. Recently, Zhou and Maskell applied background subtraction and motion compensation to solve the problem about object detection in urban video captured from aerial moving camera [13].…”

Section: Background Difference Modelingmentioning

confidence: 99%

Moving Object Detection Using Sparse Approximation and Sparse Coding Migration

Li¹,

Hu²,

Zhao³

2020

KSII TIIS

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In order to meet the requirements of background change, illumination variation, moving shadow interference and high accuracy in object detection of moving camera, and strive for real-time and high efficiency, this paper presents an object detection algorithm based on sparse approximation recursion and sparse coding migration in subspace. First, low-rank sparse decomposition is used to reduce the dimension of the data. Combining with dictionary sparse representation, the computational model is established by the recursive formula of sparse approximation with the video sequences taken as subspace sets. And the moving object is calculated by the background difference method, which effectively reduces the computational complexity and running time. According to the idea of sparse coding migration, the above operations are carried out in the down-sampling space to further reduce the requirements of computational complexity and memory storage, and this will be adapt to multi-scale target objects and overcome the impact of large anomaly areas. Finally, experiments are carried out on VDAO datasets containing 59 sets of videos. The experimental results show that the algorithm can detect moving object effectively in the moving camera with uniform speed, not only in terms of low computational complexity but also in terms of low storage requirements, so that our proposed algorithm is suitable for detection systems with high real-time requirements.

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“…For railway track beds, an ML classifier method has been proposed for recognising woody plants [15]. For detecting obstacles on the track, utilising ML technology in comparing input and reference data to train frontal view camera pictures was proposed, therein yielding accurate and successful results in experiments [16]. Moreover, to improve the detection of defects in railway fasteners for improving accuracy and overall safety, ML has been applied to image recognition on railway tracks [3], [17].…”

Section: Related Work a Railway Applications And Machine Learningmentioning

confidence: 99%