Methodology for Automatic Collection of Vehicle Traffic Data by Object Tracking

Caro-Gutierrez, Jesus; Bravo-Zanoguera, Miguel E.; González-Navarro, Félix F.

doi:10.1007/978-3-319-62434-1_39

Advances in Computational Intelligence

2017

DOI: 10.1007/978-3-319-62434-1_39

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Methodology for Automatic Collection of Vehicle Traffic Data by Object Tracking

Jesus Caro-Gutierrez¹,

Miguel E. Bravo-Zanoguera²,

Félix F. González-Navarro³

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Cited by 3 publications

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“…With the launch of these commercial video satellites, applying object tracking technologies in satellite video data has been possible. Satellite video data provide great potentials in motion analysis [32], traffic monitoring [33][34][35], suspicious object surveillance [36], and urban management etc.. Therefore, it shows great significance in studying object tracking technology on satellite video data.…”

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Object Tracking in Satellite Videos Based on a Multiframe Optical Flow Tracker

Cai

2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Object tracking is a hot topic in computer vision. Thanks to the booming of the very high resolution (VHR) remote sensing techniques, it is now possible to track targets of interests in satellite videos. However, since the targets in the satellite videos are usually too small compared with the entire image, and too similar with the background, most state-of-the-art algorithms failed to track the target in satellite videos with a satisfactory accuracy. Due to the fact that optical flow shows the great potential to detect even the slight movement of the targets, we proposed a multi-frame optical flow tracker (MOFT) for object tracking in satellite videos. The Lucas-Kanade optical flow method was fused with the HS V color system and integral image to track the targets in the satellite videos, while multi-frame difference method was utilized in the optical flow tracker for a better interpretation. The experiments with three VHR remote sensing satellite video datasets indicate that compared with state-of-the-art object tracking algorithms, the proposed method can track the target more accurately.

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Object Tracking in Satellite Videos Based on a Multiframe Optical Flow Tracker

Cai

2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Disruptor-Aware Interval-Based Response Inconsistency for Correlation Filters in Real-Time Aerial Tracking

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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

Chen

Sui

2019

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

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<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>

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Methodology for Automatic Collection of Vehicle Traffic Data by Object Tracking

Cited by 3 publications

References 8 publications

Object Tracking in Satellite Videos Based on a Multiframe Optical Flow Tracker

Object Tracking in Satellite Videos Based on a Multiframe Optical Flow Tracker

Disruptor-Aware Interval-Based Response Inconsistency for Correlation Filters in Real-Time Aerial Tracking

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

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