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In this paper, we propose an integrated system for scale-variance pedestrian detection. It consists of two cascaded components: a multi-layer detection neural network (MLDNN) for scale-variance pedestrian detection, and a fast decision forest (FDF) for boosting detection performance with only a slight decrease in speed. Experimental results on the Caltech Pedestrian dataset show that our approach...
Describing the contents of images is a challenging task for machines to achieve. It requires not only accurate recognition of objects and humans, but also their attributes and relationships as well as scene information. It would be even more challenging to extend this process to identify falls and hazardous objects to aid elderly or users in need of care. This research makes initial attempts to deal...
Fast R-CNN is a well-known approach to object detection which is generally reported to be robust to scale changes. In this paper we examine the influence of scale within the detection pipeline in the case of company logo detection. We demonstrate that Fast R-CNN encounters problems when handling objects which are significantly smaller than the receptive field of the utilized network. In order to overcome...
Vehicle recognition has been an important topic in intelligent transportation. However, to recognize different vehicle models from a same make is difficult as there are many near-identical cars under different brand names. In this paper, we investigated fine-grained vehicle recognition via deep Convolutional Neural Network (CNN). Vehicle and the corresponding parts are localized with the help of Region-based...
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