Alignment of videos recorded from moving vehicles

Serrat, Joan; Diego,; Lumbreras, Felipe; Alvarez, Fernando

doi:10.1109/iciap.2007.4362829

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Although we aim at registering nighttime sequences, we consider essential to also test the algorithms with daylight sequences. To this end, we used three sequences of each class denoted as Night1, Night2 and Night3 (Serrat et al, 2007), and as Day1, Day2 and Day3 (Kong et al, 2010) respectively. Their alignment implies a quite challenging task, since the speed of vehicles varies.…”

Section: Resultsmentioning

confidence: 99%

Night–time Outdoor Surveillance With Mobile Cameras

2012

Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

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Abstract:This paper addresses the problem of video surveillance by mobile cameras. We present a method that allows online change detection in night-time outdoor surveillance. Because of the camera movement, background frames are not available and must be "localized" in former sequences and registered with the current frames.To this end, we propose a Frame Localization And Registration (FLAR) approach that solves the problem efficiently. Frames of former sequences define a database which is queried by current frames in turn. To quickly retrieve nearest neighbors, database is indexed through a visual dictionary method based on the SURF descriptor. Furthermore, the frame localization is benefited by a temporal filter that exploits the temporal coherence of videos. Next, the recently proposed ECC alignment scheme is used to spatially register the synchronized frames. Finally, change detection methods apply to aligned frames in order to mark suspicious areas. Experiments with real night sequences recorded by in-vehicle cameras demonstrate the performance of the proposed method and verify its efficiency and effectiveness against other methods.

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Section: Resultsmentioning

confidence: 99%

Night–time Outdoor Surveillance With Mobile Cameras

2012

Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

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“…This model is quadratic in the x and y coordinates but linear in Ω and it holds under the assumption of having small angles and large focal length [13]. We estimate Ω using the well-known additive forward implementation of the LukasKanade algorithm [14] which minimizes the sum of squared differences between the target frame (observed frame) S o t and its corresponding transformed reference frame S r xt :…”

Section: B On-line Video Registrationmentioning

confidence: 99%

Vision-based road detection via on-line video registration

Diego

Álvarez

Serrat

et al. 2010

13th International IEEE Conference on Intelligent Transportation Systems

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Road segmentation is an essential functionality for supporting advanced driver assistance systems (ADAS) such as road following and vehicle and pedestrian detection. Significant efforts have been made in order to solve this task using visionbased techniques. The major challenge is to deal with lighting variations and the presence of objects on the road surface. In this paper, we propose a new road detection method to infer the areas of the image depicting road surfaces without performing any image segmentation. The idea is to previously segment manually or semi-automatically the road region in a trafficfree reference video record on a first drive. And then to transfer these regions to the frames of a second video sequence acquired later in a second drive through the same road, in an on-line manner. This is possible because we are able to automatically align the two videos in time and space, that is, to synchronize them and warp each frame of the first video to its corresponding frame in the second one. The geometric transform can thus transfer the road region to the present frame on-line. In order to reduce the different lighting conditions which are present in outdoor scenarios, our approach incorporates a shadowless feature space which represents an image in an illuminantinvariant feature space. Furthermore, we propose a dynamic background subtraction algorithm which removes the regions containing vehicles in the observed frames which are within the transferred road region.

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“…The rotation R is parameterized by the Euler angles Ω = (Ω x , Ω y , Ω z ) (pitch, yaw and roll respectively). The motion vector field associated to this homography can be approximated using the model in [10]. This model holds under the assumption of having small angles and large focal length.…”

Section: Spatial Registrationmentioning

confidence: 99%

Automatic ground-truthing using video registration for on-board detection algorithms

Álvarez

Diego

López

et al. 2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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Ground-truth data is essential for the objective evaluation of object detection methods in computer vision. Many works claim their method is robust but they support it with experiments which are not quantitatively assessed with regard some ground-truth. This is one of the main obstacles to properly evaluate and compare such methods. One of the main reasons is that creating an extensive and representative groundtruth is very time consuming, specially in the case of video sequences, where thousands of frames have to be labelled. Could such a ground-truth be generated, at least in part, automatically ? Though it may seem a contradictory question, we show that this is possible for the case of video sequences recorded from a moving vehicle. The key idea is to manually label the frames in one sequence and then be able to transfer this segmentation into another video sequence recorded at a different time on the same track, possibly under a different ambient lighting. We have carried out experiments on several video sequence pairs and quantitatively assessed the precision of the transformed ground-truth, which prove that this method is quite accurate.

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Alignment of videos recorded from moving vehicles

Abstract: We

Cited by 6 publications

References 16 publications

Night–time Outdoor Surveillance With Mobile Cameras

Night–time Outdoor Surveillance With Mobile Cameras

Vision-based road detection via on-line video registration

Automatic ground-truthing using video registration for on-board detection algorithms

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