Fusion of a camera and a laser range sensor for vehicle recognition

Mohottala, Shirmila; Ono, S.; Kagesawa, Masataka; Ikeuchi, Katsushi

doi:10.1109/cvprw.2009.5204099

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…Our work is also related to object detection methods which use trajectories [3,4], methods using the weighting of features [25], methods dealing with codebook noise [17], and methods which integrate temporal information [24].…”

Section: Related Workmentioning

confidence: 99%

Foreground Object Detection by Motion-based Grouping of Object Parts

et al. 2014

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Effective video-based detection methods are of great importance to intelligent transportation systems (ITS), and here we propose a method to localize and label objects. The method is able to detect pedestrians and bicycle riders in a complex scene. Our method is inspired by the common fate principle, which is a mechanism of visual perception in human beings, and which states tokens moving or functioning in a similar manner tend to be perceived as one unit. Our method embeds the principle in an Implicit Shape Model (ISM). In our method, keypoint-based object parts are firstly detected and then grouped by their motion patterns. Based on the grouping results, when the object parts vote for object centers and labels, each vote belonging to the same object part is assigned a weight according to its consistency with the votes of other object parts in the same motion group. Afterwards, the peaks, which correspond to Z. Wang ( ) · M. Kagesawa · S. Ono · K. Ikeuchi detection hypotheses on the Hough image formed by summing up all weighted votes, become easier to find. Thus our method performs better in both position and label estimations. Experiments show the effectiveness of our method in terms of detection accuracy.

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Section: Related Workmentioning

confidence: 99%

Foreground Object Detection by Motion-based Grouping of Object Parts

et al. 2014

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“…The LiDAR and camera can capture the same region because of their mounting positions. Mohottala et al (MOHOTTALA et al , 2009) observed initially that the segmentation based only on the LiDAR sensor was sometimes not possible. For example, the data obtained from a dark object was not clear.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…However, Mohottala et al (MOHOTTALA et al , 2009) used a technique that can achieve robust segmentation even when the background color distributions are not well separated from the objects.…”

Section: Review Of Related Workmentioning

confidence: 99%