Fast obstacle detection using targeted optical flow

Boroujeni, Nasim Sepehri; Etemad, S. Ali; Whitehead, Anthony

doi:10.1109/icip.2012.6466796

Cited by 15 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prior to 2012 tracking approaches based on generative models are widely applied such as meanshift (Vojir, Noskova, & Matas, 2013), Kalman filter (Chen, 2012), optical flow (Boroujeni, 2012) and particle filter (Baxter, Leach, & Robertson, 2014). The generative model treats the tracking as a searching problem to find a region most similar to the target.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Tracking in Satellite Videos via Joint Discrimination and Pose Estimation

Chen

Sui

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

<p><strong>Abstract.</strong> Object tracking has gained much attention in the field of computer vision and intelligent traffic analysis. Satellite videos are more suitable for long-distance tracking comparing to the road traffic videos. However, most of the state-of-the-art methods produce poor results when applied to satellite videos, due to low resolution of the small target and interference from similar background in satellite videos. In this paper, we present an improved Discriminative Correlation Filter based approach specifically tailored for small objects tracking in satellite videos through applying spatial weight in the filter and estimating the pose by Kalman filter. First, a spatial mask is introduced to encourage the final filter to give different contributions depending on the spatial distance. Furthermore, Kalman filter is incorporated into the approach with the aim of predicting the position when the target run into the large area similar background region. Finally, an efficient strategy for combining the improved DCF tracker and pose estimation is proposed. Experimental results on three satellite videos describing the traffic conditions of three cities demonstrate that the proposed approach can effectively track the targets even though the targets are similar to the background region in a period of time. Compare with other state-of-the-art methods, the final accuracies and speeds of our method achieve the best.</p>

show abstract

Section: Introductionmentioning

confidence: 99%

Real-Time Tracking in Satellite Videos via Joint Discrimination and Pose Estimation

Chen

Sui

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Usually, the step outputs a binary mask that divides the inputted image into regions of foreground and background. In the recent past, moving object detection has become a hot topic, and a lot of methods have been proposed to deal with the problem [9–31]. These methods can be roughly classified into three categories: optical flow based method [9, 10], inter‐frame difference method [11] and background subtraction method [12–31].…”

Section: Introductionmentioning

confidence: 99%

“…In the recent past, moving object detection has become a hot topic, and a lot of methods have been proposed to deal with the problem [9–31]. These methods can be roughly classified into three categories: optical flow based method [9, 10], inter‐frame difference method [11] and background subtraction method [12–31]. The optical flow based methods detect moving objects by using dense optical flow field maps of the inputted video.…”

Section: Introductionmentioning

confidence: 99%

Background segmentation of dynamic scenes based on dual model

Yin

Zhang

Wang

2014

IET Computer Vision

View full text Add to dashboard Cite

Detecting moving objects from background in video sequences is the first step of many image applications. The background can be divided into two types according to whether the pixel values of it are variable or not: static one and dynamic one. How to correctly detect moving foreground objects from dynamic scenes is a difficult problem because of the similarity between the moving foreground and the variable background. In this study, a new method for non‐parametric background segmentation of dynamic scenes is proposed. Here the background is described by two interrelated models. One of them is called the self‐model, which concerns with the recently observed pixel values at the same position, and the other one is called the neighbourhood‐model, which is described by the pixel values of the neighbourhood. The author's method can accurately detect the dynamic background. To correctly detect pixels in the foreground as much as possible, the authors also propose an adaptive threshold for foreground decision based on the background characteristics. All of the above detection processes can be done in real time. Experimental results on public dataset demonstrate that the proposed method outperforms the state‐of‐the‐art for background segmentation in dynamic scenes.

show abstract

Moving target detection approach based on spatio-temporal salient perception

Jin

et al. 2014

Optik

View full text Add to dashboard Cite

Fast obstacle detection using targeted optical flow

Cited by 15 publications

References 9 publications

Real-Time Tracking in Satellite Videos via Joint Discrimination and Pose Estimation

Real-Time Tracking in Satellite Videos via Joint Discrimination and Pose Estimation

Background segmentation of dynamic scenes based on dual model

Moving target detection approach based on spatio-temporal salient perception

Contact Info

Product

Resources

About