As a popular research topic, pedestrian detection has played an important role in both Intelligent Transportation Systems and Autonomous Vehicles. It is also the basis of pedestrian tracking and behaviour recognition. The most used sensors monocular camera, stereo camera and combining cameras with LIDAR for pedestrian detection all have their own drawbacks. Compared with data acquired by these sensors, RGB-D images have the following main advantages: (1) by incorporating depth information on the basis of RGB images, they can resist bad light condition to some degree; (2) they can be collected faster and more steadily than stereo images; (3) they contain more dense depth data than LIDAR. All these advantages of RGB-D images attract huge researchers’ enthusiasm. However, due to the limits of sensors, the researches based on RGB-D images are all currently limited to the indoor scenes using Kinect. There is no published paper on pedestrian detection in outdoor images collected by other RGB-D sensors. Motivated by the advantages of RGB-D images, we conduct our research work to improve the performance of pedestrian detection based on RGB-D images. The main contributions of this paper are as follows. · A proper solution to collect RGB images and depth images outdoors is proposed. By creatively proposing an algorithm based on the pinhole camera model, we solve the image registration problem and thus we can collect RGB-D images outdoors. · Due to the lack of public RGB-D based pedestrian dataset outdoor, we create a RGB-D pedestrian detection dataset, which contains over12000 RGB-D images and are with detailed annotations. · A novel local variance based adaptive threshold algorithm to de-noise the depth images is proposed. The experiment shows that it can de-noise the depth image more effectively than Gaussian and Laplacian filters. · We conduct our research and experiments on RGB-D based pedestrian detection algorithms. A novel depth-guided pedestrian detection framework to improve the detection speed and accuracy is proposed.
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