Research Article Open Access

Pedestrian Detection in RGB-D Data Using Deep Autoencoders

Pavel Aleksandrovich Kazantsev1 and Pavel Vyacheslavovich Skribtsov1
  • 1 PAWLIN Technologies Ltd, Dubna, Russia


Recent popularity of RGB-D sensors mostly comes from the fact that RGB-images and depth maps supplement each other in machine vision tasks, such as object detection and recognition. This article addresses a problem of RGB and depth data fusion for pedestrian detection. We propose pedestrian detection algorithm that involves fusion of outputs of 2D- and 3D-detectors based on deep autoencoders. Outputs are fused with neural network classifier trained using a dataset which entries are represented by pairs of reconstruction errors of 2D- and 3D-autoencoders. Experimental results show that fusing outputs almost totally eliminate false accepts (precision is 99.8%) and brings recall to 93.2% when tested on the combined dataset that includes a lot of samples with significantly distorted human silhouette. Though we use walking pedestrians as objects of interest, there are few pedestrian-specific processing blocks in this algorithm, so, in general, it can be applied to any type of objects.

American Journal of Applied Sciences
Volume 12 No. 11, 2015, 847-856


Submitted On: 23 September 2015 Published On: 17 November 2015

How to Cite: Kazantsev, P. A. & Skribtsov, P. V. (2015). Pedestrian Detection in RGB-D Data Using Deep Autoencoders. American Journal of Applied Sciences, 12(11), 847-856.

  • 2 Citations



  • Pedestrian Detection
  • Deep Autoencoders
  • Data Fusion
  • RGB-D
  • Image Processing