Learning transmodal person detectors from single spectral training sets

Kieritz, Hilke; Hübner, Wolfgang; Arens, Michael

doi:10.1117/12.2028651

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…A non-maximum suppression is then used to group positive classifications to the final detections. A similar pipeline is applied in later pedestrian detection methods based on integral channel features (Dollár et al, 2009(Dollár et al, , 2014Benenson et al, 2012), which have been shown to work also in infrared images (Kieritz et al, 2013).…”

Section: Image-based Pedestrian Detectionmentioning

confidence: 99%

Data Processing and Recording Using a Versatile Multi-Sensor Vehicle

Borgmann

Schatz

Kieritz

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> In this paper we present a versatile multi-sensor vehicle which is used in several research projects. The vehicle is equipped with various sensors in order to cover the needs of different research projects in the area of object detection and tracking, mobile mapping and change detection. We show an example for the capabilities of this vehicle by presenting camera- and LiDAR-based pedestrian detection methods. Besides this specific use case, we provide a more general in-depth description of the vehicle’s hard- and software design and its data-processing capabilities. The vehicle can be used as a sensor carrier for mobile mapping, but it also offers hardware and software components to allow for an adaptable onboard processing. This enables the development and testing of methods related to real-time applications or high-level driver assistance functions. The vehicle’s hardware and software layout result from several years of experience, and our lessons learned can help other researchers set up their own experimental platform.</p>

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Section: Image-based Pedestrian Detectionmentioning

confidence: 99%

Data Processing and Recording Using a Versatile Multi-Sensor Vehicle

Borgmann

Schatz

Kieritz

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…3: For person detection several feature channels are computed. The detection is based on decision trees using integrals over the different feature channels [15], [16]. A soft-cascade is used to speed up the detection.…”

Section: A Person Detectionmentioning

confidence: 99%

“…An outline of the person detector can be seen in Figure 3. Detection is done by a sliding window approach using a classifier consisting of weighted decisions trees which are selected by boosting [14], [15]. Each node of a tree uses the sum of a fixed region I of a feature channel to make its decision [16].…”

Section: A Person Detectionmentioning

confidence: 99%

Hierarchical Hough forests for view-independent action recognition

Hilsenbeck

Münch

Kieritz

et al. 2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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Appearance-based action recognition can be considered as a natural extension of appearance-based object detection from the spatial to the spatio-temporal domain. Although this step seems natural, most action recognition approaches are evaluated in isolation. Towards this end the contribution of this paper is twofold. First, a view-independent approach to action recognition is proposed and second the sensitivity w.r.t. a combination of person detection and action recognition is evaluated. Action recognition is performed in a hierarchical manner: First, the relative camera orientation in the scene is estimated and second, the action is determined using view-dependent Hough forests. The proposed approach is evaluated on the multi-view i3DPost dataset [1] and its performance is compared to single-step approaches using Hough forests. The results suggest that the recognition rate increases, when using the proposed hierarchical method compared to single-step approaches. Further, the performance rates of hierarchical Hough forests on ground truth data are compared to the results of hierarchical Hough forests in combination with a person detector.

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“…Furthermore, structures on the persons clothing or other surfaces are not captured in IR which makes IR advantageous for appearance based methods. For the spatial domain Kieritz et al showed that a person detector based on Integral Channel Features (ICFs) provides promising results in both, the visible and the infrared spectrum [3]. Action recognition systems however operate in the spatiotemporal domain.…”

Section: Introductionmentioning

confidence: 99%

Action Recognition in the Longwave Infrared and the Visible Spectrum Using Hough Forests

Hilsenbeck

Münch

Grosselfinger

et al. 2016

2016 IEEE International Symposium on Multimedia (ISM)

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Action recognition in surveillance systems has to work 24/7 under all kinds of weather and lighting conditions. Towards this end, most action recognition systems only work in the visible spectrum which limits their general usage to daytime applications. In this work Hough forests are applied to the longwave infrared spectrum which can capture humans both in the dark and in daylight. Further, Integral Channel Features which have shown promising results in the spatial domain are applied to the spatio-temporal domain and are incorporated into the Hough forest approach. This approach is evaluated on a new outdoor dataset containing different violent and non-violent actions recorded in the visible and infrared spectrum. It is further shown that for the visible spectrum the proposed approach achieves state-of-the-art results on the KTH and i3DPost dataset.

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Learning transmodal person detectors from single spectral training sets

Cited by 7 publications

References 14 publications

Data Processing and Recording Using a Versatile Multi-Sensor Vehicle

Data Processing and Recording Using a Versatile Multi-Sensor Vehicle

Hierarchical Hough forests for view-independent action recognition

Action Recognition in the Longwave Infrared and the Visible Spectrum Using Hough Forests

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